The Great Reset: Why '5 Years LLM Experience' Is Mathematically Impossible

• Tags: AI Career Industry

Series: Why AI Unicorns Don’t Exist
Part 2 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 Mathematically Impossible (You are here)
Part 3 Stop Chasing Unicorns, Start Building Orchestras Read →
Part 4 Building AI Teams That Actually Work Read →

The entire field reset in 2022. Everyone became a beginner again. Including the “experts.”

The Timeline Problem

In Part 1, I showed you job postings asking for impossible combinations of skills.

But there’s a deeper problem that makes AI expertise uniquely impossible—not just difficult.

Let me show you with a simple question:

When did the LLM era begin?

November 2022

ChatGPT launched on November 30, 2022.

That’s when generative AI went from research curiosity to industry transformation.

Today is late 2025.

The field is 36 months old.

Now look at the job postings again:

“3+ years focused on AI/ML projects”

“5+ years of experience in AI-related roles”

If you started learning LLMs the day ChatGPT launched—November 30, 2022—and studied every single day since then, you would have 36 months of experience.

Not 5 years.

36 months. Maximum.

But It’s Worse Than That

The LLM era didn’t just add new skills to learn.

It made previous skills obsolete.

This wasn’t evolution. It was extinction.

What Happened to NLP Research

Here’s what the Natural Language Processing field looked like before and after:

Before November 2022:

├─ BERT fine-tuning
├─ Named Entity Recognition models
├─ Sentiment analysis pipelines
├─ Question answering benchmarks
├─ Custom model training
├─ Dataset-specific architectures
├─ Token-level optimization
└─ Published in top conferences for years

After November 2022:

├─ LLM API orchestration
├─ RAG architectures
├─ Prompt engineering
├─ Agent frameworks
├─ Context window management
├─ Tool use and planning
├─ Evaluation of probabilistic outputs
└─ Completely different field

These aren’t the same skills with new names.

They’re different skills entirely.

The Extinction Event

Let me be specific about what became obsolete:

What Died:
├─ Years of BERT research → Rarely used now
├─ Fine-tuning expertise → Less relevant (prompting often enough)
├─ Custom NER models → LLMs do it out of box
├─ Dataset curation for training → Still matters, but different
├─ Benchmark optimization → Production behavior matters more
├─ Most PhD theses (2015-2022) → Outdated approaches
└─ Entire conference tracks → Abandoned or pivoted

A PhD student who spent 2017-2022 mastering BERT fine-tuning?

Their deep expertise became nearly worthless in six months.

Not diminished. Not less valuable.

Nearly worthless.

Who Became Beginners

Here’s the devastating part:

Everyone reset to zero. Including the “experts.”

Tenured Professors:

I know NLP professors at top universities who told their students in early 2023:

“I’ve spent 15 years becoming an expert in this field. I have to admit—I’m learning alongside you now.”

Twenty years of experience. Hundreds of papers. Academic prestige.

Reset to beginner.

PhD Students (2015-2022 cohort):

Five years deep in transformer research. Dissertation nearly complete. Published at top venues.

Had to pivot or become obsolete.

Some finished degrees in approaches that were already outdated by graduation.

Industry NLP Engineers:

Ten years building NLP systems. Expert in spaCy, Hugging Face fine-tuning, production pipelines.

Had to learn a completely new paradigm.

Their production experience still helped. But their core technical expertise? Reset.

The Unlearning Problem

It wasn’t just learning new things.

They had to unlearn the old things first.

What Experts Had to UNLEARN:
├─ Fine-tuning mindset → Use pretrained models as-is
├─ Dataset-centric research → Prompt-centric approach
├─ Model architecture focus → System design focus
├─ Training expertise → API orchestration
├─ Benchmark optimization → Real-world evaluation
└─ Their entire mental model

Unlearning is harder than learning.

When you’re an expert, your instincts are trained. You pattern-match to solutions that worked before.

But the old patterns don’t apply anymore.

Expertise became a liability.

The Math That Proves Impossibility

Let’s be precise:

ChatGPT Launch: November 30, 2022
Current Date: Late 2025
Maximum Possible Experience: ~36 months

Job Posting Requirement: "5+ years LLM experience"
5 years = 60 months

The Gap: 24 months of experience that CANNOT EXIST

This isn't difficult.
This is LITERALLY IMPOSSIBLE.

Even “3 years experience” is barely possible—only if you started the exact month ChatGPT launched and have done nothing else since.

Who Actually Has Real Experience?

Let’s map out the realistic landscape:

Tier 1: Built the Technology (< 500 people globally)

├─ Worked at OpenAI, Anthropic, Google DeepMind, Meta AI
├─ Built GPT-4, Claude, Gemini, Llama
├─ Actual multi-year experience (internal R&D predates public launch)
├─ Earning $400K - $1M+
└─ Not applying to your job posting

Tier 2: Early Adopters (~10,000 people)

├─ Started experimenting December 2022
├─ Built real systems through 2023-2024
├─ Actual experience: 24-30 months
├─ Honest about being in transition
└─ Your best realistic candidates

Tier 3: Recent Learners (millions of people)

├─ Started learning 2023-2024
├─ Completed courses, tutorials, bootcamps
├─ Actual experience: 6-18 months
├─ May claim more on resume
└─ Need support and growth time

When someone claims “5 years of LLM experience,” they’re either:

  1. One of ~500 people who worked at frontier labs (not applying to your role)
  2. Conflating old ML experience with LLM experience (not the same thing)
  3. Exaggerating (consciously or not)

There is no option 4.

What People Are Actually Doing

So what happens when companies post “5 years LLM experience required”?

Candidates adapt their resumes:

Resume Says:
"5+ years experience in AI/ML and LLMs"

Reality:
├─ 2018-2021: Deep learning with NLP research
├─ 2021-2022: Some BERT fine-tuning
├─ 2023: Started learning about LLMs
├─ 2024: Built 2-3 RAG projects
├─ Actual LLM experience: ~18 months

What They Did:
Grouped all "AI-adjacent" work as "AI/ML experience"

Is this dishonest?

It’s complicated.

The job posting asked for something impossible. The candidate adapted. The recruiter filters for keywords. Everyone pretends the math works.

The system creates the lies it complains about.

Why Previous Tech Waves Were Different

Let’s compare to earlier “unicorn” demands:

Cloud Computing (2006-2010):

AWS launched: 2006
Companies wanted "cloud experts": 2010
Time for expertise to develop: 4 years ✓

Real experts existed.
Requirements were achievable.

Data Science (2012-2017):

"Sexiest job" article: 2012
Peak demand for data scientists: 2017
Time for expertise to develop: 5 years ✓

Real experts existed.
Requirements were achievable.

LLMs / Generative AI (2022-2025):

ChatGPT launched: November 2022
Companies want "5 years experience": 2024
Time available: 24 months ✗

NOT ENOUGH TIME.
Requirements are impossible.

Previous waves had time to create experts before demand peaked.

This wave didn’t.

The demand arrived 24 months after the technology launched.

The Advantage Inversion

Here’s something counterintuitive:

Traditional NLP expertise provides minimal advantage.

The people thriving in the LLM era aren’t necessarily the NLP researchers.

They’re:

✓ Strong software engineers
  (System design transfers directly)

✓ Product-minded builders  
  (Know how to ship real things)

✓ Domain experts who can code
  (Legal, medical, finance + Python)

✓ System thinkers
  (Orchestration is the core skill)

✓ Rapid learners
  (Field changes monthly)

The hierarchy inverted.

A software engineer with 2 years of LLM focus often outperforms an NLP PhD with 10 years of pre-LLM research.

Because the PhD has to unlearn. The engineer just learns.

A Real Example From the Wild

I found this post on Threads recently. Someone describing their actual AI journey:

“My deep learning study path: MLP → CNN → Segmentation → Object Detection → Transformer → LLM

About 3 years.

And that’s how I became my company’s only AI expert. My role changed, salary up ~50% in 3 years.”

Three years. Standard learning progression. Became the company’s AI expert. 50% salary increase.

This is what realistic AI growth looks like.

Now compare to the job postings:

This Person’s Reality Job Posting Fantasy
3 years learning path “5+ years LLM experience”
Standard progression (CNN → Transformers → LLM) “Expert in CV, NLP, RAG, agents, MLOps…”
Became company’s “only AI expert” Would be rejected as “too junior”
50% salary increase, role expanded Demands PhD + 10 years IT experience

This person IS what companies actually need.

But they probably wouldn’t pass the job posting filter.

The job postings are rejecting exactly the people who could help.

What This Means for Job Postings

When you see:

“Required: 5+ years experience with LLMs and generative AI”

You’re looking at one of three situations:

Situation 1: Ignorance

├─ HR doesn't understand field timeline
├─ Copied requirements from other postings
├─ Doesn't realize the impossibility
└─ Will get zero truly qualified applicants

Situation 2: Hoping for OpenAI Alumni

├─ Wants someone who built GPT-4
├─ Those people earn $500K+
├─ Won't apply for $200K role
└─ Will get zero truly qualified applicants

Situation 3: Accepting the Fiction

├─ Knows requirement is impossible
├─ Will accept inflated resumes
├─ Hires based on interview performance
├─ Gets someone with 18 months real experience
└─ May or may not work out

None of these is a good hiring strategy.

The Uncomfortable Truth

Let me state this plainly:

There are no AI unicorns because there hasn’t been TIME to create them.

Cloud computing had 4+ years to develop expertise before peak demand.

Data science had 5+ years.

LLMs had 36 months.

No amount of individual brilliance can compress 5 years into 3 years.

The expertise companies are demanding literally does not exist in the talent pool.

So What Actually Works?

If unicorns don’t exist—can’t exist, by timeline—then what?

The answer isn’t lowering standards.

The answer is changing the model.

Instead of hunting for one person who knows everything (impossible), build systems where:

  • Different people contribute different expertise
  • Knowledge compounds over time
  • Specialists support each other
  • Realistic roles enable realistic hiring

That’s Part 3.

Next: “Stop Chasing Unicorns, Start Building Orchestras”