Sorting 2026 AI Vocabulary

One Map First

Least confusing organization of modern AI concepts:

DATA CENTER MODELS (Frontier LLMs)
        ↑
AI WORKSTATIONS / EDGE SERVERS
        ↑
SMALL LANGUAGE MODELS (SLMs)
        ↑
AI AGENTS (software behavior layer)
        ↑
AI APPLIANCES (products using models)

Key insight: most new terms describe layers, not brand-new kinds of intelligence.

1) Small Language Models (SLMs)

Your intuition (“AI appliances and agents using condensed models”) is very close. SLMs are the model optimized for low memory, low power, fast inference, and often narrower competence.

What changed recently

Small models used to be “inferior mini GPTs.” Now they’re often intentionally trained specialists and can collaborate with larger models when needed.

Definition (non-marketing)

SLM = a model optimized for:

• low memory & low power
• fast inference
• narrower competence
• local/private operation

Rough size bands

Translation to your terms

AI appliance → often runs an SLM AI agent → may use an SLM SLM → the model itself

2) AI Agents

Agents are mostly an old idea made practical by modern LLMs, better tool use, and cheaper inference. An agent is not a model—it’s software that decides when and how to use models and tools.

Best mental model

Agent = Orchestrator
Model = Brain
Tools = Hands

3) Model Context Protocol (MCP)

This one is genuinely important: it standardizes how AI systems access tools and local data safely. Your “firewall” instinct is partly right.

Core idea

User
  ↓
MCP Host   (app you use)
  ↓
MCP Client (translator)
  ↓
MCP Server (controlled capability)

Better analogy

MCP ≈ “USB standard” for AI capabilities (not intelligence).

Refined: MCP = API gateway + permission broker + capability firewall for what the AI is allowed to touch.

4) AI Workstations (Grace Blackwell, etc.)

Moving into hardware tiers: “Grace Blackwell” describes a tightly integrated CPU+GPU superchip architecture designed for large-model inference and training at enterprise/workstation scales.

Is there competition?

Yes—accelerator ecosystems are now a real race:

5) Vera Rubin — Where it fits

“Vera Rubin” is positioned as a next-generation step beyond “Blackwell,” aimed primarily at hyperscale AI clusters and frontier workloads. Conceptually:

SLMs → edge PCs
Workstations → prosumers / teams
Blackwell → enterprise
Vera Rubin → hyperscale datacenters

So: mostly data center class (not home labs, at least for now).

6) Liquid Cooling & “Delidding” CPUs

Liquid cooling (usually not radical)

chip → metal plate → coolant loop

Delidding (enthusiast / extreme)

Taking the heat spreader off reduces thermal resistance (often improving temps), but the risk is high and it’s not typical of mainstream AI. Your skepticism is justified.

7) Multi-agent “swarms” for coding

Real, but heavily hyped. Typical pattern:

Planner Agent
   ↓
Coder Agent
   ↓
Tester Agent
   ↓
Reviewer Agent

Works best on constrained tasks; still fragile; orchestration matters. The real advances are reliable tool use, shared context/memory, and standardization (like MCP).

8) Nano Claw & personal AI hosts

These belong in a “personal AI host” category: persistent local assistant, privacy-first, hybrid local/cloud intelligence— combining an SLM locally with occasional remote LLM calls and agent orchestration.

What’s real vs. mostly marketing

One-sentence summary

2026 AI is transitioning from one giant centralized brain to a distributed ecosystem of small local models, coordinated agents, and occasional calls to massive datacenter models.