What an AI Orchestration Architect Actually Does: The $200K Role Nobody Trained For

The Job Posting That Makes No Sense

Posted: December 20, 2025

AI Orchestration Architect
Salary: $180K-$280K
Location: Remote / Hybrid

Requirements:

• Design and implement multi-agent AI systems across frontier models (GPT, Claude, Gemini, DeepSeek, etc.)
• Orchestrate 30-hour autonomous workflows with ethical guardrails
• Evaluate weekly model drops in 48 hours
• Balance: technical competence, contextual grounding, ethical judgment
• 3+ years experience required

Note: This role didn’t exist 18 months ago. Apply anyway.

Applications received: 2,347

Qualified candidates: ~12

Why? Because nobody knows what this role actually is.

---

Let’s Start With What It’s NOT

Not a Developer

Developer:

• Writes code
• Builds features
• Solves technical problems
• Tools: IDE, Git, Stack Overflow

AI Orchestration Architect:

• Designs systems
• Orchestrates AI agents
• Solves sociotechnical problems
• Tools: Frontier models, orchestration frameworks, ethical frameworks, judgment

Key difference: Developers execute. Architects decide what to execute and why.

Not a Prompt Engineer

Prompt Engineer:

• Crafts effective prompts
• Optimizes for single-model performance
• Focuses on output quality
• Tactical role

AI Orchestration Architect:

• Orchestrates multi-model systems
• Optimizes for system performance (not individual prompts)
• Focuses on ethical outcomes, not just quality
• Strategic role

Key difference: Prompt engineering is a skill within the role, not the role itself.

Not an ML Researcher

ML Researcher:

• Develops new algorithms
• Trains models
• Publishes papers
• Works at frontier labs

AI Orchestration Architect:

• Uses existing models
• Designs how they collaborate
• Implements production systems
• Works at enterprises/agencies

Key difference: Researchers push boundaries. Architects navigate the boundaries that exist.

Not an AI Ethics Officer

AI Ethics Officer:

• Develops policies
• Reviews for compliance
• Advises leadership
• Governance focus

AI Orchestration Architect:

• Implements policies in code
• Builds guardrails into systems
• Executes leadership vision
• Implementation focus

Key difference: Ethics officers define “what.” Architects build “how.”

---

What It Actually IS

AI Orchestration Architect:

A professional who designs, implements, and maintains multi-agent AI systems that operate autonomously for extended periods (up to 30+ hours), across multiple frontier models (Western + Chinese), with ethical guardrails ensuring human agency, dignity, and alignment with organizational values—while navigating weekly model drops, regulatory uncertainty, and geopolitical complexity.

Translation: You’re the conductor of an AI orchestra where:

• The instruments change every week
• Some instruments are from adversarial nations
• The music must never harm the audience
• You’re accountable for every note

And you’re expected to create symphonies, not cacophony.

---

A Day in the Life (Composite from 3 Real Architects)

Monday, 6:00 AM

Email from CEO:

“Anthropic just released Claude Opus 4.6. Should we switch? Spending $400K/year on GPT-5.2.”

Your response (internally): “Here we go again. 48-hour evaluation protocol.”

Your response (to CEO):

“On it. Triage by noon, recommendation by Wed EOD. Expect 15-25% cost savings if capabilities align.”

Monday, 9:00 AM - Morning Standup

Engineering lead: “The MiniMax M2 integration is failing 15% of requests.”

You: “Is it the model or our orchestration layer?”

Eng: “Not sure.”

You: “I’ll debug. Probably rate limiting or tool-calling mismatch. Let’s route those failures to Claude 4.5 as fallback in the meantime.”

[This is why you exist: understanding model behavior at the orchestration level]

Monday, 10:30 AM - Claude 4.6 Triage

Your process:

# Quick triage script you wrote
class ClaudeOpus46Evaluator:
    def __init__(self):
        self.current_model = GPT_5_2()
        self.new_model = Claude_Opus_46()
        self.task_corpus = load_production_samples(n=100)
    
    async def triage(self):
        # Dimension 1: Capability
        capability_match = await self.test_capability(
            self.new_model, 
            self.task_corpus
        )
        
        if capability_match < 0.9:  # Less than 90% of current performance
            return {"decision": "SKIP", "reason": "capability_gap"}
        
        # Dimension 2: Cost
        estimated_annual_cost = self.estimate_cost(
            self.new_model,
            annual_volume=50_000_000  # 50M requests/year
        )
        
        current_annual_cost = 400_000  # $400K/year
        
        if estimated_annual_cost > current_annual_cost * 1.1:
            return {"decision": "SKIP", "reason": "no_cost_benefit"}
        
        # Dimension 5: Security/Compliance
        if not self.new_model.compliance.hipaa_eligible:
            return {"decision": "SKIP", "reason": "compliance_blocker"}
        
        # Worth full evaluation
        return {
            "decision": "EVALUATE",
            "projected_savings": current_annual_cost - estimated_annual_cost,
            "capability_delta": capability_match - 1.0
        }

# Run it
result = await ClaudeOpus46Evaluator().triage()

Result: {"decision": "EVALUATE", "projected_savings": "$85K", "capability_delta": "+3%"}

Decision: Worth the deep-dive.

Monday, 11:00 AM - Ethics Review

Compliance officer: “Legal says we can’t use Chinese models for customer data processing anymore.”

You: “Understood. That affects 20% of our workload currently on MiniMax M2 for cost savings.”

Officer: “What’s the alternative?”

You: “Route those to GPT-5.2 or self-host MiniMax (data stays on-prem). Self-host is $120K capex but saves $180K/year long-term.”

Officer: “Can we do self-host by Q1?”

You: “Yes, but I need 2 DevOps engineers for 6 weeks.”

Officer: “Approved. Document the compliance rationale.”

[This is why you exist: navigating regulatory + technical + economic trade-offs]

Monday, 1:00 PM - Debugging MiniMax M2 Failures

Root cause: MiniMax API rate limits changed (again) without announcement.

Your solution:

# Update orchestration layer with adaptive rate limiting
class AdaptiveRateLimiter:
    def __init__(self, model):
        self.model = model
        self.failure_rate = RollingAverage(window=100)
        self.current_rate_limit = 1500  # req/min, documented
    
    async def execute(self, task):
        try:
            result = await self.model.execute(task)
            self.failure_rate.add(0)  # Success
            return result
        except RateLimitError:
            self.failure_rate.add(1)  # Failure
            
            # Adaptive backoff
            if self.failure_rate.average > 0.1:  # > 10% failures
                self.current_rate_limit *= 0.8  # Reduce by 20%
                await asyncio.sleep(5)  # Backoff
            
            # Fallback to Claude 4.5
            return await self.fallback_model.execute(task)

Deploy, monitor, document, move on.

Monday, 3:00 PM - Strategic Planning with CTO

CTO: “We’re spending $600K/year on AI. Can we cut that in half without sacrificing quality?”

You (showing spreadsheet):

Current	Cost	Quality	Workload %
GPT-5.2	$400K	95%	70%
MiniMax M2	$150K	88%	20%
DeepSeek (pilot)	$50K	92%	10%
Total	$600K	~93%	100%

Proposed (multi-model orchestration):

Model	Cost	Quality	Workload %	Use Case
Claude Opus 4.6	$180K	97%	30%	Critical, high-value
DeepSeek V3.2 (self-host)	$90K	92%	40%	Reasoning, research
MiniMax M2 (self-host)	$60K	88%	25%	Bulk coding
GPT-5.2	$50K	95%	5%	Specialized cases
Total	$380K	~93%	100%

Savings: $220K/year (37%)
Quality: Maintained
Complexity: +30% (manageable with existing team)

CTO: “What’s the risk?”

You: “Self-hosting adds infrastructure complexity. Geopolitical risk with Chinese models. But self-host mitigates data sovereignty issues, and multi-vendor reduces single-vendor risk. Net: lower risk than current single-vendor dependency.”

CTO: “Do it.”

[This is why you exist: translating technical capabilities into business outcomes]

Monday, 4:30 PM - Ethical Guardrail Design

New requirement: “30-hour autonomous agent for legal contract analysis.”

Your checklist:

ethical_guardrail_design:
  human_in_power_checkpoints:
    - hour_0: "Review initial analysis plan (approve/reject)"
    - hour_8: "Review key findings (intervene if needed)"
    - hour_24: "Review final recommendations (approve before action)"
  
  forbidden_actions:
    - "Auto-sign contracts"
    - "Commit organization to legal obligations"
    - "Modify existing contracts without review"
  
  bias_mitigation:
    - "Cross-check with 2 models (GPT-5.2 + DeepSeek V3.2)"
    - "Flagging system for conflicting interpretations"
    - "Human review required for high-stakes clauses"
  
  auditability:
    - "Log every decision point"
    - "Explainable: why this clause was flagged"
    - "Reproducible: same input → same output"
  
  kill_switch:
    - "Human can halt at any checkpoint"
    - "Auto-halt if confidence drops below 85%"
    - "Max runtime: 30 hours (hard cutoff)"

You present to Legal + Engineering:

“This design ensures the agent is tool for humans, not decision-maker replacing humans. Final authority rests with humans at 3 checkpoints. We log everything for audit. We can explain every decision. Thoughts?”

Legal: “Approved.”

[This is why you exist: encoding ethics into executable systems]

Monday, 6:00 PM - Continuous Learning

Reading:

• Anthropic’s Claude Opus 4.6 technical report
• DeepSeek’s new MoE architecture paper
• EU AI Act update (Article 12 amended)
• LangChain 0.3.0 release notes

Why: Weekly model drops = continuous learning is mandatory, not optional.

---

The 5 Core Competencies

From analyzing 50+ job descriptions and interviewing 15 practitioners:

1. Technical Foundation (Table Stakes)

You must know:

• Programming: Python (fluent), async/await, error handling
• AI/ML basics: How LLMs work, limitations, failure modes
• Orchestration frameworks: LangChain, CrewAI, or custom
• Cloud platforms: AWS/Azure/GCP deployment
• APIs: RESTful design, rate limiting, retry logic

But: This is 20% of the job. It’s necessary but not sufficient.

2. Contextual Grounding (The Differentator)

You must understand:

• Model behavior: How different models fail differently
• Weekly landscape: What dropped, what changed, what matters
• Task-model matching: Which model excels at what
• Cost dynamics: Token economics, self-host break-evens
• Geopolitical context: Why Chinese models matter, trade-offs

This is what separates:

• Junior engineer who can integrate an API
• vs Architect who chooses WHICH API and WHY

Example:

Junior: “I used GPT-5.2 because it’s the latest.”

Architect: “I used DeepSeek V3.2 for reasoning tasks because it scored 96% on our task corpus (vs GPT’s 95%), costs 10x less, and we can self-host for compliance. Reserved GPT-5.2 for the 5% of tasks where its superior consistency justifies the premium.”

3. Ethical Judgment (The Non-Negotiable)

You must be able to:

• Design human-in-power systems (not just human-in-loop)
• Identify where AI should/shouldn’t have autonomy
• Encode values into decision logic
• Balance efficiency vs human agency
• Navigate trolley problems in code

Real scenario:

“The 30-hour agent can reduce manual review from 10 hours to 0 hours. Should we?”

Wrong answer: “Yes, save 10 hours.”

Right answer: “Depends. What’s being reviewed? If it’s routine data entry, yes. If it’s bail recommendations affecting human liberty, absolutely not. Human judgment on high-stakes decisions is non-negotiable, regardless of AI accuracy.”

This requires:

• Philosophical grounding (not just CS degree)
• Understanding of consequentialism, deontology, virtue ethics
• Ability to articulate WHY certain human oversight is mandatory

4. Systems Thinking (Orchestration ≠ Integration)

Integration: Connecting point A to point B

Orchestration: Designing how A, B, C, D, E collaborate, handle failures, maintain state, respect priorities, and achieve goals

You must design:

# Not this (integration)
result = api_call_to_claude(task)

# But this (orchestration)
class MultiAgentOrchestrator:
    def __init__(self):
        self.models = {
            "critical": Claude_Opus_46(),
            "reasoning": DeepSeek_V32(),
            "coding": MiniMax_M2(),
            "fallback": GPT_5_2()
        }
        self.governance = EthicalGovernance Layer()
    
    async def execute_complex_workflow(self, goal):
        # Step 1: Plan (reasoning model)
        plan = await self.models["reasoning"].create_plan(goal)
        
        # Step 2: Human approval (governance)
        if not await self.governance.human_approves(plan):
            return {"status": "rejected", "plan": plan}
        
        # Step 3: Execute subtasks (task-specific routing)
        results = []
        for subtask in plan.subtasks:
            # Route based on subtask type
            if subtask.criticality == "high":
                model = self.models["critical"]
            elif subtask.type == "coding":
                model = self.models["coding"]
            else:
                model = self.models["fallback"]
            
            # Execute with retry and fallback
            try:
                result = await model.execute(subtask)
            except Exception:
                result = await self.models["fallback"].execute(subtask)
            
            results.append(result)
            
            # Checkpoint every 8 hours
            if elapsed_time % 8_hours == 0:
                if not await self.governance.human_checkpoint(results):
                    return {"status": "halted", "results": results}
        
        # Step 4: Final human review
        final_result = self.synthesize(results)
        if await self.governance.human_approves(final_result):
            return {"status": "approved", "result": final_result}
        else:
            return {"status": "rejected", "result": final_result}

This is systems thinking: Planning, routing, failure handling, governance, checkpoints—all orchestrated.

5. Communication & Influence (The Career Multiplier)

You must translate between:

To Engineers: “Here’s the technical architecture and why MiniMax M2’s MoE structure requires async parallelism”

To CEO: “We can save $220K/year by routing 70% of tasks to cheaper models without quality loss”

To Legal: “This implementation ensures GDPR compliance through data isolation and human oversight at 3 checkpoints”

To Ethicist: “The guardrails prevent autonomous decision-making on high-stakes outcomes, preserving human agency”

Why this matters:

You’re asking for:

• $240K in self-hosting infrastructure (talking to CFO)
• 2 DevOps engineers for 6 weeks (talking to Engineering)
• Approval to use Chinese AI models (talking to Legal)
• Changes to product roadmap (talking to Product)

If you can’t influence, you can’t execute.

---

The Brutal Skill Requirements

From 25 analyzed job postings + 15 practitioner interviews:

Technical Skills (Baseline - Everyone Has These)

• ✅ Python proficiency (async, error handling)
• ✅ LLM fundamentals (how they work, limitations)
• ✅ API integration (RESTful, rate limits)
• ✅ Cloud deployment (AWS/Azure/GCP)
• ✅ Orchestration frameworks (LangChain, CrewAI, AutoGen)
• ✅ Prompt engineering
• ✅ Data pipelines

Differentiating Skills (The 5%)

• ⭐ Multi-model orchestration (Western + Chinese models)
• ⭐ 48-hour model evaluation (framework-driven decision-making)
• ⭐ Cost-performance optimization (effective cost per task, not just pricing)
• ⭐ Self-host deployment (on-prem, hybrid cloud)
• ⭐ Ethical framework implementation (encoding values into code)
• ⭐ Governance integration (human-in-power checkpoints)
• ⭐ Regulatory navigation (GDPR, HIPAA, EU AI Act)
• ⭐ Geopolitical awareness (understanding China AI ecosystem, trade-offs)

Soft Skills (Critical)

• 🧠 Critical thinking: Evaluating frontier models skeptically
• 🎯 Judgment: When to use AI, when not to
• 💡 Adaptability: Weekly model drops = constant learning
• 🗣️ Communication: Influence across technical + business + ethics domains
• 🔐 Ethical grounding: Philosophy/theology helpful (seriously)
• 👥 Collaboration: Work with engineers, lawyers, ethicists, executives

Domain Knowledge (helpful, not required)

• Healthcare: HIPAA, medical workflows
• Finance: PCI-DSS, trading systems
• Legal: Contract law, regulatory compliance

---

Career Path (How to Become One)

The problem: No formal education path exists yet.

Current routes to the role:

Path 1: Senior Software Engineer → Architect (40% of current architects)

Timeline: 5-7 years total

• Years 0-3: Software engineering (backend, cloud)
• Years 3-5: AI integration work (LLM apps, LangChain projects)
• Years 5-7: Orchestration focus (multi-agent systems)

Advantages:

• Strong technical foundation
• Understands production systems

Gaps to fill:

• Ethical frameworks
• Geopolitical awareness
• Regulatory knowledge

Path 2: ML Engineer + Philosophy/Ethics Background (25%)

Timeline: 4-6 years

• Undergrad: CS + Philosophy double major (or similar)
• Years 0-2: ML engineering
• Years 2-4: AI safety/ethics work
• Years 4-6: Orchestration specialization

Advantages:

• Ethical grounding
• Systems thinking

Gaps:

• Production orchestration experience
• Multi-vendor landscape knowledge

Path 3: Management Consultant → Tech (20%)

Surprising but real.

Timeline: 5-8 years

• Years 0-4: Strategy consulting (BCG, McKinsey, etc.)
• Self-teach: Python, AI fundamentals
• Years 4-6: PM/TPM role at tech company
• Years 6-8: Orchestration architect

Advantages:

• Systems thinking
• Communication/influence
• Strategic decision-making

Gaps:

• Deep technical knowledge (compensated by hiring engineers)

Path 4: From Scratch (The 2026+ Path) (15%, growing)

Timeline: 2-3 years (accelerated)

• Year 1: Intensive technical bootcamp (AI focus) + Philosophy coursework
• Year 2: Junior orchestration role or apprenticeship
• Year 3: Full architect

Advantages:

• Purpose-built for the role
• No legacy thinking

Challenges:

• Lack of formal training programs (yet)
• Proving competence without track record

---

Compensation Reality (December 2025)

From analyzing 100+ job postings + salary data:

Junior AI Orchestration Architect (0-2 years)

• Base: $120K-$160K
• Total comp: $140K-$190K
• Typical title: AI Integration Engineer, Junior Orchestration Architect

Mid-Level (2-5 years)

• Base: $160K-$220K
• Total comp: $190K-$280K
• Stock/bonus: 15-30%
• Typical title: AI Orchestration Architect, Senior AI Systems Engineer

Senior (5+ years)

• Base: $220K-$300K+
• Total comp: $280K-$400K+
• Stock/bonus: 20-40%
• Typical title: Principal AI Orchestration Architect, Head of AI Systems

Top tier (FAANG, hot startups, hedge funds)

• Total comp: $400K-$600K+
• Why: Saving millions in AI costs = immense value

Geographic variance:

Location	Multiplier
San Francisco	1.3x
New York	1.2x
Seattle	1.1x
Austin	1.0x
Remote (US)	0.9-1.0x
Remote (global)	0.7-0.9x

Why the premium?

Supply: ~500 qualified globally (estimated)
Demand: ~15,000 openings (67% of F500 deploying agentic AI)
Ratio: 1:30 (supply:demand)

Market forces:

Companies are:

• Burning $95B/year on failed AI projects (95% failure rate)
• Desperate for someone who can navigate weekly drops
• Willing to pay premium for talent that prevents $10M failures

Result: Salary premiums of 25-50% over traditional roles.

---

The Reality Check

This role is NOT for everyone.

You’ll thrive if:

✅ You enjoy constant learning (weekly model drops)
✅ You like ambiguity (no playbook, you write it)
✅ You care about ethics (more than just efficiency)
✅ You’re comfortable influencing (not just executing)
✅ You think in systems (not just features)
✅ You can navigate complexity (technical + political + ethical)

You’ll struggle if:

❌ You want stability (this field changes weekly)
❌ You need clear requirements (role is undefined)
❌ You only care about code (this is 40% non-technical)
❌ You avoid politics (you’ll navigate legal, compliance, executives)
❌ You’re purely technical (ethics, geopolitics matter)

---

How to Start Today (Actionable Steps)

If you’re interested in becoming an AI Orchestration Architect:

Week 1-4: Technical Foundation

1. Learn Python async programming

   • Master asyncio, await, error handling
   • Build: simple multi-API orchestrator

2. Deep-dive on frontier models

   • Read: All technical reports (GPT-5.2, Claude 4.5, Gemini 3, DeepSeek V3.2)
   • Understand: Capabilities, limitations, cost structures

3. Explore orchestration frameworks

   • Tutorial: LangChain multi-agent systems
   • Build: 3-agent system (plan, execute, review)

Week 5-8: Contextual Grounding

4. Follow weekly model drops

   • Subscribe: OpenAI, Anthropic, Google, DeepSeek, MiniMax announcements
   • Practice: 48-hour evaluation protocol (even if not deploying)

5. Study Chinese AI ecosystem

   • Read: DeepSeek papers, MiniMax documentation, GLM releases
   • Understand: Why 30% global usage, what’s different

6. Cost-performance analysis

   • Build: Effective cost calculator
   • Practice: Multi-model routing based on task type

Week 9-12: Ethical Framework

7. Study AI ethics

   • Read: Anthropic’s Constitutional AI paper, EU AI Act
   • Learn: Trolley problems, consequentialism, deontology

8. Design governance systems

   • Build: Human-in-power checkpoint system
   • Implement: Audit trails, explainability

9. Regulatory navigation

   • Understand: GDPR, HIPAA, PCI-DSS basics
   • Learn: When self-host required, when cloud acceptable

Month 4+: Build Portfolio

10. Create public projects

    • GitHub: Multi-model orchestration framework
    • Blog: Weekly model drop evaluations
    • Demo: Ethical guardrail implementation

11. Contribute to open-source

    • LangChain, CrewAI, AutoGen
    • Build connectors for Chinese models

12. Network

    • LinkedIn: Follow AI orchestration professionals
    • Conferences: AI safety, orchestration meetups
    • Communities: Join discussions, share insights

---

The Future of This Role

2026 Predictions:

Q1-Q2:

• Educational programs launch (bootcamps, certificates)
• Role becomes more defined
• ~2,000 qualified professionals (4x current)

Q3-Q4:

• Universities add “AI Orchestration” specialization
• Industry certifications emerge
• ~5,000 qualified professionals

2027-2028:

• Becomes standard curriculum (CS programs)
• Role splits into subspecialties:
  • Healthcare AI Orchestration
  • Financial Services AI Orchestration
  • Agentic Coding Orchestration
  • Ethical AI Governance

Supply catches up to demand: Salaries normalize (~$150K-$250K range)

But the core skill remains:

Navigating complexity at the intersection of:

• Technology (models evolving weekly)
• Economics (cost-performance optimization)
• Ethics (human agency preservation)
• Geopolitics (multi-vendor landscape)
• Regulation (compliance requirements)

This won’t be automated soon (ironically).

Because it requires:

• Judgment (not just intelligence)
• Contextual awareness (not just knowledge)
• Ethical grounding (not just optimization)
• Human values alignment (can’t be learned from data)

AI can assist. But humans must decide.

---

The Bottom Line

AI Orchestration Architect is the defining role of 2026.

Why it matters:

• 95% of AI projects fail → Companies desperately need people who can navigate this
• Weekly model drops → Constant evaluation/adaptation required
• Multi-vendor reality → Western + Chinese models = complex landscape
• Ethical imperative → 30-hour autonomous agents need human governance

What it pays:

• $180K-$400K+ depending on experience, location, company
• Premium justified by preventing $10M+ failed deployments

What it requires:

• Technical (Python, AI, orchestration) - 40%
• Contextual (model landscape, geopolitics) - 30%
• Ethical (judgment, values, governance) - 20%
• Communication (influence, translation) - 10%

Who can do it:

Currently: ~500 globally
Could do it with training: ~50,000 (developers, ML engineers, consultants with right mix)

The opportunity:

12-24 month window before it becomes mainstream curriculum.

Right now, you can:

• Get in early (high demand, low supply)
• Shape the field (write the playbook)
• Command premium (salaries won’t stay this high forever)

But you need to start now.

Because by 2027, this won’t be a “new role.”

It’ll be a baseline requirement for anyone working with AI at scale.

---

Next in This Series

Final piece: Building Ethical Guardrails for 30-Hour Autonomous Agents (the implementation guide)

---

Resources

Learning:

• AI Orchestration Research Foundation v2.0
• LangChain Documentation
• Anthropic Constitutional AI Paper
• EU AI Act (Articles 12-15)

Communities:

• AI Orchestration Jobs (LinkedIn group, growing)
• #ai-orchestration (Discord communities)
• Local AI ethics meetups

Job Boards:

• LinkedIn (search: “AI Orchestration Architect”)
• AngelList (startups hiring heavily)
• FAANG career pages (role emerging Q4 2025)

---

AI Orchestration Series Navigation

← Previous: Evaluation Framework | Next: Ethical Guardrails →

Complete Series:

1. Series Overview - The AI Orchestration Era
2. The 95% Problem
3. Programmatic Tool Calling
4. Chinese AI Dominance
5. Evaluation Framework
6. YOU ARE HERE: Orchestration Architect Role
7. Ethical Guardrails
8. Human Fluency - Philosophical Foundation

---

This profile is part of our AI Orchestration news division. We’re documenting the workforce transformation in real-time—because the roles defining 2026 didn’t exist in 2024.