Stop Chasing Unicorns, Start Building Orchestras
Series: Why AI Unicorns Don’t Exist
Part 3 of 4
📚 Unicorn Series Navigation
| Part | Title | Link |
|---|---|---|
| Part 1 | From Handsome Horses to Actual Unicorns | Read → |
| Part 2 | The Great Reset (Why ‘5 Years LLM Experience’ Is Impossible) | Read → |
| Part 3 | Stop Chasing Unicorns, Start Building Orchestras | (You are here) |
| Part 4 | Building AI Teams That Actually Work | Read → |
Quick Recap
In Part 1, I showed you job postings demanding 20+ specializations in one person.
In Part 2, I proved the math: the field is 36 months old, but companies want 5 years of experience. Impossible by definition.
Today: What actually works.
The company that built GPT doesn’t hire unicorns. Here’s what they do instead.
The Question Everyone’s Avoiding
If unicorns don’t exist at scale—can’t exist, by timeline—what do we do?
Most companies are stuck in this loop:
Post impossible job requirements
→ No qualified applicants
→ Lower standards quietly
→ Hire someone who "checks boxes"
→ Project struggles
→ Blame "AI is hard"
→ Repeat
There’s a better way.
But it requires rethinking what “AI Solutions Architect” actually means.
The Wrong Definition
Here’s how most companies define the role:
“An AI Solutions Architect must be expert in: machine learning, deep learning, data engineering, cloud infrastructure, MLOps, security, deployment, monitoring, stakeholder management…”
This demands a unicorn.
So companies hire people who claim expertise in everything.
But actually have tutorial-level knowledge in each area.
Then everyone pretends it’s working.
Until it isn’t.
The Right Definition
An architect should be a connector, not a master.
Think of an orchestra conductor.
A conductor doesn’t need to be the best violinist, the best cellist, and the best pianist.
They need to understand how all instruments work together to create a symphony.
Same with AI architects.
They shouldn’t be expected to be:
- ML expert
- Data engineering expert
- Infrastructure expert
- Security expert
They should be:
- Coordinators who understand enough to ask good questions
- Synthesizers who see how pieces fit together
- Facilitators who make specialists effective
- Decision-makers who navigate trade-offs
The job is orchestration, not solo performance.
Proof: How OpenAI Actually Hires
You’d think the company that built GPT would demand the most from their Solutions Architects.
Here’s their actual job posting:
OpenAI Solutions Architect
“We are looking for a solutions-oriented technical leader to engage with customers post-sale and ensure they realize tangible business value from their investment.”
What they ask for:
- 5+ years of technical consulting, post-sales engineering, or solutions architecture
- Strong communicator, able to explain concepts to executive and practitioner audiences
- Hands-on proficiency in Python, JavaScript, or similar
- Comfortable building prototypes or proofs of concept
- Takes end-to-end ownership, proactively acquiring new skills as needed
- Humble, collaborative mindset
- Thrives in fast-paced environments
Salary: $220K – $280K + Equity
Notice what’s missing:
❌ No "PhD preferred"
❌ No "10+ years infrastructure experience"
❌ No "Expert in TensorFlow AND PyTorch AND scikit-learn"
❌ No "React frontend experience"
❌ No "Triple cloud certified"
❌ No "Deep understanding of transformer architectures"
❌ No "Published research"
OpenAI—the company that invented GPT—doesn’t ask for these.
Because they know it’s not the job.
The Contrast
Let me put these side by side:
| Random Enterprise Posting | OpenAI Posting |
|---|---|
| 20+ specializations required | Core coordination skills |
| “Expert in everything” | “Hands-on proficiency” |
| PhD preferred | No degree requirement mentioned |
| 5 years LLM experience (impossible) | 5 years consulting/solutions work |
| Work overtime implied | Normal expectations |
| Proves nothing about AI understanding | Written by people who built the technology |
Who understands AI expertise better?
The company demanding React skills from their AI architect?
Or the company that created ChatGPT?
What OpenAI Knows
Read their language carefully:
“Trusted advisors and technical partners” — Relationship and guidance, not raw technical dominance
“Help customers build and execute their AI adoption strategy” — Strategic thinking, not implementation of everything
“Architectural guidance, building hands-on prototypes” — Direction and demonstration, not solo delivery
“Humble, collaborative mindset” — Knows when to bring in specialists
“Proactively acquiring new skills as needed” — Admits they don’t know everything upfront
OpenAI has specialists internally. World-class researchers. Infrastructure experts. The Solutions Architect’s job is to connect customers to the right resources and guide the journey.
Not to be every resource themselves.
The Three-Tier System
Here’s what actually works in organizations that deliver AI successfully:
Tier 1: Solutions Architects (Coordinators)
What They Need:
├─ Conceptual understanding of AI/ML
├─ Deep expertise in 1-2 specific areas
├─ 20% working knowledge in adjacent domains
├─ Strong communication skills
├─ Strategic and systems thinking
├─ Know when to escalate
└─ Honest about limitations
What They Don't Need:
├─ Implement transformers from scratch
├─ Understand every research paper
├─ Be the smartest person in the room
└─ Solve every technical problem alone
Training Time: 200-400 hours to add coordination skills
(on top of existing specialist background)
Their Job:
├─ Understand client problems holistically
├─ Design sensible high-level architecture
├─ Coordinate specialists effectively
├─ Manage expectations realistically
├─ Make strategic trade-offs
└─ Trust and leverage the team
They’re not unicorns. They’re skilled coordinators.
Tier 2: AI Engineers (Builders)
What They Need:
├─ Strong programming fundamentals
├─ Experience with common AI/ML patterns
├─ Familiarity with frameworks (LangChain, etc.)
├─ Can handle 80% of standard use cases
├─ Know when they're stuck
└─ Document learnings for the team
What They Don't Need:
├─ Novel research breakthroughs
├─ Solve every edge case alone
├─ Be thought leaders
└─ Present to executives constantly
Training Time: 800-1,200 hours
Their Job:
├─ Build using established patterns
├─ Follow architectural guidance
├─ Implement with quality and speed
├─ Recognize when problem is novel
├─ Escalate to specialists when needed
└─ Contribute to team knowledge
They’re not unicorns. They’re solid builders.
Tier 3: Domain Specialists (Experts)
What They Have:
├─ Deep expertise in specific domain
├─ Can read and implement research papers
├─ Novel problem-solving ability
├─ Handle the hard 20% of problems
└─ Can innovate when needed
What They Don't Need:
├─ Client management skills
├─ Present to stakeholders constantly
├─ Scale to 10 projects simultaneously
└─ Be generalists
Their Job:
├─ Solve problems nobody else can
├─ Support multiple teams as needed
├─ Build reusable patterns and tools
├─ Mentor Tier 2 over time
├─ Create institutional knowledge
└─ Handle complexity so others don't have to
They’re not unicorns. They’re deep specialists.
You only need 1-2 per 10-20 projects.
How It Works Together
┌─────────────────────────────────────┐
│ Client / Business Need │
└─────────────────┬───────────────────┘
│
▼
┌─────────────────────────────────────┐
│ Tier 1: Solutions Architect │
│ │
│ • Understands the problem │
│ • Designs high-level approach │
│ • Coordinates the team │
│ • Manages stakeholders │
└─────────────────┬───────────────────┘
│
▼
┌─────────────────────────────────────┐
│ Tier 2: AI Engineers │
│ │
│ • Build the solution │
│ • Handle standard patterns (80%) │
│ • Escalate novel problems │
│ • Document what they learn │
└─────────────────┬───────────────────┘
│
▼ (only when stuck)
┌─────────────────────────────────────┐
│ Tier 3: Domain Specialists │
│ │
│ • Solve the hard problems (20%) │
│ • Create new patterns │
│ • Unblock the team │
│ • Build institutional knowledge │
└─────────────────┬───────────────────┘
│
▼
┌─────────────────────────────────────┐
│ Knowledge System │
│ │
│ • Patterns get documented │
│ • Solutions become reusable │
│ • Team gets smarter over time │
│ • Competitive advantage grows │
└─────────────────────────────────────┘
The key insight: Knowledge flows back into the system.
Every hard problem solved becomes a pattern. Every pattern makes future projects faster. The organization learns, not just individuals.
Real Example: The Difference
Project: Enterprise RAG System for Legal Firm
Approach A: Chase the Unicorn
Week 1-2:
├─ Hire "AI Expert" at $350K
├─ Claims expertise in everything
├─ Builds demo using tutorials
└─ Client impressed with prototype
Week 3-4:
├─ Client wants production system
├─ Needs: Complex document processing
├─ Needs: Citation preservation
├─ Needs: Multi-jurisdiction compliance
├─ Expert realizes: "This is harder than expected"
└─ Starts working nights
Week 5-8:
├─ Expert struggles alone
├─ Googling frantically
├─ No one to escalate to
├─ Reinventing solutions that exist
└─ Falling behind schedule
Week 10-12:
├─ System unreliable
├─ Hallucinations in legal context (dangerous)
├─ Missing citations (unusable)
├─ Expert burns out
├─ Project cancelled or "pivoted"
└─ $200K+ wasted, no learnings captured
Success Rate: ~20%
Knowledge Retained: Zero
Approach B: Build the Orchestra
Week 1:
├─ Tier 1 (Solutions Architect) meets client
├─ Deeply understands requirements
├─ Identifies key challenges:
│ ├─ 100+ page documents
│ ├─ Citation preservation critical
│ └─ Legal precedent linking needed
├─ Consults Tier 3 specialist (2-hour session)
├─ Gets guidance on known approaches
└─ Designs architecture using proven patterns
Week 2-3:
├─ Tier 2 (AI Engineers) start building
├─ Use company's RAG template
├─ Apply documented best practices
├─ 60% of requirements done quickly
├─ Get stuck on citation preservation
└─ Escalate with clear documentation
Week 4:
├─ Tier 3 (Specialist) reviews problem (2 hours)
├─ Identifies solution approach
├─ Builds specialized module (1 week)
├─ Documents pattern for future use
├─ Trains Tier 2 on the approach
└─ Total specialist time: ~40 hours
Week 5-8:
├─ Tier 2 completes implementation
├─ Tests across document types
├─ Iterates based on feedback
├─ Specialist available for questions
└─ Progress steady and predictable
Week 9-10:
├─ Production deployment
├─ System working reliably
├─ Client satisfied
├─ Knowledge documented
├─ Pattern available for next legal client
└─ Foundation for faster future projects
Success Rate: ~75%
Knowledge Retained: Permanently
Next Similar Project: 40% faster
The Math
10 projects per year:
| Approach | Success | Cost per Success | Knowledge |
|---|---|---|---|
| Unicorn Hunt | 2 projects (20%) | ~$750K | Lost when person leaves |
| Three-Tier | 7-8 projects (75%) | ~$300K | Compounds over time |
The difference: ~$4M in value per year
Plus: The three-tier system gets better over time. The unicorn hunt stays stuck.
Why Specialists Should Like This
A common fear: “This system devalues specialists.”
The opposite is true.
In the unicorn model:
- Specialists are told they’re “too narrow”
- Forced to pretend they know everything
- Set up to fail outside their expertise
- No clear career path that values depth
In the three-tier model:
- Specialists are valued for their depth
- Called in for hard problems (where they shine)
- Not forced to do work outside their expertise
- Clear role with clear value
Being world-class at one thing is a feature, not a bug.
The system needs depth. The system rewards depth.
Depth just doesn’t have to come from one person trying to be deep in everything.
The Honest Architect
Imagine an architect who says:
“I understand ML at a conceptual level, but I’m not implementing the model. That’s why we have specialists.
I understand deployment constraints, but I’m not configuring infrastructure. That’s why we have platform engineers.
My job is to ensure their requirements and constraints are compatible—and to make the right trade-offs when they’re not.”
This is honest.
This is realistic.
This is what OpenAI hires for.
This is what actually works.
But There’s a Problem
If this system is so obvious, why don’t more companies do it?
Because there’s a structural trap that makes it hard to implement.
Companies create the exact problem they’re trying to solve:
- They structure teams by specialization (necessary)
- This prevents engineers from gaining breadth (structural)
- They reject internal candidates for “lacking breadth” (ironic)
- They hire external generalists with shallow knowledge (mistake)
- The generalists rely on internal specialists (reality)
- Specialists get frustrated and leave (predictable)
- Company loses real expertise (disaster)
The system that creates expertise also blocks its recognition.
That’s Part 4: How to actually build this system, fix the promotion trap, and make it work in your organization.
Next: “The Circular Trap: How to Actually Build an AI Team”