When most people think of AI today, they think of ChatGPT or another Large Language Model (LLM). But here’s the reality: LLMs are only one piece of the puzzle. And while they’ve been stealing the spotlight, other specialized AI models are quietly reshaping what’s possible.

Here’s something worth pausing on: Gartner expects that by 2026, over 60% of enterprise AI deployments will lean on domain-specific or multimodal models —not just LLMs. That means the future of AI will look less like one oversized “brain” and more like an ecosystem of specialists working together.

The best way to think about it? Medicine. A family doctor is great, but when you need heart surgery, you want a cardiologist. AI is following the same path: generalists for broad coverage, specialists for critical problems.

This post was inspired by a thoughtful LinkedIn post from Manthan Patel. He highlighted these 8 architectures, and I wanted to expand on them — especially with examples relevant for business leaders.

1. LLMs (Large Language Models)

These are the ones you already know: GPT, Claude, Gemini. They handle text one token at a time, enabling everything from storytelling to reasoning.

• Why it matters: They power chatbots, knowledge assistants, and content generation tools.

• Example: A legal team using AI to summarize hundreds of case files in minutes.

LLMs are the generalists —flexible and powerful, but not always the most efficient tool for the job.

2. LCMs (Large Concept Models)

LCMs (like Meta’s SONAR) don’t just look at words — they encode whole sentences or ideas as “concepts.” That makes them better at capturing meaning.

• Why it matters: They handle intent and context at a higher level.

• Example: A search engine that knows when you ask about “jaguar” you mean the car, not the animal.

3. VLMs (Vision-Language Models)

These models combine vision and text. They can interpret images and generate meaningful text descriptions. See work like CLIP from OpenAI for an example.

• Why it matters: Businesses increasingly need AI that “sees” as well as “reads.”

• Example: An e-commerce app where you upload a picture of a pair of shoes, and it finds the closest match in inventory.

4. SLMs (Small Language Models)

SLMs are the lighter cousins of LLMs. They’re optimized for edge devices — phones, cars, and IoT systems. You can read more about the rise of SLMs.

• Why it matters: Not everything can run in the cloud. SLMs make real-time, on-device AI possible.

• Example: A car’s voice assistant that works even without an internet connection.

Sometimes smaller really is smarter — efficiency can be more valuable than raw power.

5. MoEs (Mixture of Experts)

MoEs activate only the parts of the model that are needed. Think of them as “experts on call.” Researchers first explored this approach in Mixture of Experts models.

• Why it matters: They deliver efficiency and scale without using unnecessary compute.

• Example: An enterprise system where financial questions trigger one set of experts and HR questions another.

MoEs are like hiring a specialist only when you need them —saving both money and energy.

6. MLMs (Masked Language Models)

These were the early workhorses of NLP (think BERT). They learn by predicting missing words in sentences, considering context from both directions.

• Why it matters: Still excellent for understanding meaning, classification, and recommendation tasks.

• Example: A recommendation engine for an online store.

7. LAMs (Large Action Models)

LAMs don’t just understand language —they take action. They can execute tasks, making them more like AI “agents.” Stanford’s HAI has been discussing their potential.

• Why it matters: They’re the missing link between knowing and doing.

• Example: A system admin assistant that doesn’t just suggest cloud optimizations —it actually implements them.

Analogy: If LLMs write the recipe, LAMs step into the kitchen and start cooking.

8. SAMs (Segment Anything Models)

SAMs excel at computer vision. They can identify and segment objects within an image down to the pixel. Meta recently released its Segment Anything Model to push this forward.

• Why it matters: Industries that rely on visuals — manufacturing, healthcare, automotive —need this level of precision.

• Example: A factory system that flags defective items on the assembly line.

Traditional AI vs. Specialized AI

Traditional AI:

• One architecture stretched across many tasks

• Often strong in one area but weak in others

• Heavy compute and data requirements

Specialized AI:

• Tailored for specific problems

• Optimized for efficiency, speed, or precision

• Unlocks new abilities like visual segmentation, task execution, and concept-level reasoning

Why This Matters for Business Leaders

The question is no longer “Which LLM should I use?” but: “Which model architecture actually fits my problem?”

Three things to keep in mind:

1. Specialized ≠ niche. These models are becoming standard across industries.

2. Efficiency matters. SLMs and MoEs can trim costs without losing performance.

3. It’s an ecosystem. The future of AI is not one giant model but a set of specialists working together.

While the market chases “the best LLM,” the smarter strategy might be asking: “Which specialist will solve this problem faster, cheaper, and more reliably?”

The winners won’t just adopt AI. They’ll pick the right specialist for the right job.

So let me ask you: Which of these specialized architectures would make the biggest impact in your business today?