Is BERT still relevant now that LLMs like GPT-4 are everywhere? Confused about what actually matters

Let me clarify the technical landscape.

The model family tree:

Transformer (2017)
├── BERT-style (encoders - understand text)
│   ├── BERT (Google, 2018)
│   ├── RoBERTa (Meta)
│   ├── MUM (Google, 2021)
│   └── Many others
└── GPT-style (decoders - generate text)
    ├── GPT series (OpenAI)
    ├── Claude (Anthropic)
    ├── Gemini (Google)
    └── Many others

BERT is still relevant, but:

1. It’s part of a larger stack now
2. Google uses MUM for more complex understanding
3. The specific model matters less than what you’re optimizing for

What actually matters:

The practical takeaway:

“Optimizing for BERT” was always about writing natural, context-rich content. That hasn’t changed. The specific model names don’t matter for your optimization strategy.

SEO perspective on the BERT evolution.

The BERT era (2019-2021):

• Focus on natural language
• Understanding user intent
• Context over keywords
• Long-tail query matching

The MUM/AI era (2021-present):

• Everything BERT did, plus…
• Multimodal understanding
• Multi-step reasoning
• AI-generated answers

What changed in practice:

Honestly? Not much for content strategy.

The advice was always:

1. Understand what users want
2. Answer their questions directly
3. Write naturally
4. Cover topics comprehensively

This worked for BERT. It works for MUM. It works for GPT.

What IS new:

The citation/extraction layer. GPT-style models need to extract and cite your content, not just match it to queries.

This requires:

• More structured formatting
• Clearer answer blocks
• More explicit expertise signals

But the natural language foundation is the same.

Content strategy perspective.

How I explain this to clients:

“BERT was about Google understanding what you mean. GPT is about AI using what you wrote.”

The practical difference:

For traditional search (BERT/MUM understanding):

• Match content to query intent
• Use natural language
• Cover related subtopics
• Build topical authority

For AI answers (GPT extraction):

• Provide extractable answer blocks
• Structure for easy citation
• Include specific data/facts
• Make expertise clear

The overlap:

Both reward:

• Quality content
• Natural language
• Comprehensive coverage
• Clear structure

My recommendation:

Don’t think in terms of “optimizing for BERT vs GPT.” Think: “How do I create content that language models can understand (BERT) AND extract/cite (GPT)?”

The answer is the same: clear, natural, well-structured, expert content.

Research perspective on the evolution.

Where BERT fits now:

BERT was foundational - it taught the industry that bidirectional context understanding works. Google hasn’t “replaced” BERT; they’ve evolved it.

The evolution:

1. BERT - Understanding queries better
2. T5 - Understanding + generation
3. MUM - Multimodal, multilingual understanding
4. PaLM/Gemini - Reasoning + generation at scale

For Google Search specifically:

Google uses multiple models in their ranking stack:

• BERT-style models for query understanding
• MUM for complex query handling
• Various models for passage ranking
• Now AI Overviews layer on top

What this means for you:

The specific model doesn’t matter for your strategy. What matters is that all these models:

1. Understand natural language better than keyword matching
2. Consider context and intent
3. Prefer clear, authoritative content
4. Can recognize expertise signals

Optimize for these principles, not for specific model names.

Technical writing perspective.

What changed in my writing from BERT to AI era:

BERT era focus:

• Natural language (not keyword stuffing)
• Answering the question (not dancing around it)
• Context for terms (defining jargon)
• Related topic coverage

Added for AI era:

• Summary blocks at top of sections
• Bulleted key points
• Definition boxes for terms
• FAQ sections matching common queries
• More explicit data/numbers

What stayed the same:

• Writing quality
• Expertise demonstration
• Natural flow
• Comprehensive coverage

My practical workflow:

1. Write naturally and comprehensively (serves BERT/traditional search)
2. Add structure and extraction points (serves GPT/AI citations)
3. Include expertise signals (serves both)

The BERT principles are the foundation. AI optimization is the enhancement layer.

Practical consultant perspective.

What I tell clients about BERT:

“Don’t worry about BERT specifically. Focus on these principles that all modern search systems share…”

The timeless principles:

1. Write for humans first - Natural language, not robotic
2. Answer the question - Direct, clear answers
3. Demonstrate expertise - Show you know the topic
4. Be comprehensive - Cover the topic fully
5. Structure logically - Clear headings, organized flow

What’s changed for AI:

Added emphasis on:

• Extractable answer formats
• Cited facts and data
• Clear entity identification
• Schema markup

The bottom line:

“BERT optimization” was marketing speak for “write naturally and answer questions.” That still applies. You’re just adding AI extraction optimization on top now.

Data perspective on BERT-related changes.

Tracking content performance across eras:

We tracked 1,000 pieces of content from 2019-2025:

BERT era (2019-2021):

• Natural language content: +35% rankings
• Keyword-stuffed content: -40% rankings

MUM/AI era (2021-2025):

• Natural + structured content: +45% visibility
• Natural but unstructured: +15% visibility
• Keyword-stuffed: -60% visibility

The pattern:

Natural language writing (the BERT principle) remains foundational. But structure for AI extraction provides additional lift.

Practical implication:

Don’t abandon BERT principles. Build on them with AI-friendly structure.

What we use:

Am I Cited to track which content formats get cited most by AI. Helps identify what structure works beyond just natural language.

This cleared up my confusion. Summary:

Is BERT still relevant?

Yes, but as a foundation, not a specific optimization target. The principles BERT represented (natural language, context, intent) are still crucial.

What’s changed:

• Multiple models work together now
• AI extraction is a new layer
• Structure matters more for AI citations

What I’m doing:

1. Keep doing: Natural language, comprehensive coverage, intent matching
2. Add: Structured formats for AI extraction, clear answer blocks, FAQ sections

The mental model:

BERT = Foundation (understanding) GPT = Layer on top (extraction and citation)

Both reward the same core qualities. AI just adds structure requirements.

Thanks everyone - much clearer now.