---
type: source
title: "How to DOMINATE with AI in 2025"
description: "To stay ahead as AI accelerates into 2025, invest in a small set of high-leverage, tool-agnostic capabilities (agents, reasoning LLMs, local LLMs, tech-stack selection, working with LLMs) rather than mastering specific tools that get replaced every week."
youtube_id: wAzBl6xllzE
url: https://www.youtube.com/watch?v=wAzBl6xllzE
slug: how-to-dominate-with-ai-in-2025
published: 2025-01-01
duration: "0:28:50"
recency_rank: 129
raw: "../raw/how-to-dominate-with-ai-in-2025.md"
tags: [strategy, capabilities, agents, "2025"]
created: 2026-07-21
updated: 2026-07-21
---

# How to DOMINATE with AI in 2025

Cole opens by naming the core anxiety of the AI era: models, frameworks, and coding assistants ship every week, making it impossible to know where to invest learning time without it going stale. His answer is a single mental-model shift borrowed from a former VP: focus on capabilities, not tools. Capabilities are the higher-leverage, framework-agnostic skills (how to architect an agent, how to prompt a reasoning model, when to go local) that stay relevant as the underlying tools churn. You still must pick some tools to actually build, but mastering them should never be the priority.

He then walks a roadmap of the high-leverage skills he sees mattering most in 2025. AI agents are the headline: 2024's advances laid the groundwork, and the skill to build is agent architecture and best practices (citing Anthropic's 'Building Effective Agents' article and its patterns: prompt chaining, routing, parallelization, orchestrator-workers, and evaluator loops). Reasoning LLMs (o1/o3, QwQ, Gemini 2.0 Flash Thinking) are second: chain-of-thought / inference-time compute makes existing models far more capable and tackles hallucinations, and the future is agentic workflows that mix slow-but-powerful reasoning models with small fast models. Local LLMs are a discipline of their own (hardware, fine-tuning, privacy, cost, speed, flexibility), with the gap to closed models closing fast via DeepSeek V3, Qwen, and Llama.

The back half covers choosing an AI tech stack (keep it simple and DRY, don't over-research every option; he shares his own volatile stack as an example, not a prescription) and working with LLMs directly: prompt engineering techniques, using AI coding assistants, human-in-the-loop for agent approvals, and leveraging massive context windows. He ties it together with his vision of an open-source AI enablement stack and closes on a non-skill that he says is just as important: finding an AI community to learn alongside.

## Concepts covered

- [Capabilities Over Tools](../concepts/capabilities-over-tools.md)
- [Skills](../concepts/skills.md)
- [Agentic Workflow Engineering](../concepts/agentic-workflow-engineering.md)
- [Agent Architecture Patterns](../concepts/agent-architecture-patterns.md)
- [Reasoning Models](../concepts/reasoning-models.md)
- [Model Selection](../concepts/model-selection.md)
- [Local & Self-Hosted AI](../concepts/local-ai.md)
- [Fine-Tuning](../concepts/fine-tuning.md)
- [Rapid Prototyping](../concepts/rapid-prototyping.md)
- [Prompt Engineering](../concepts/prompt-engineering.md)
- [Agentic Coding](../concepts/agentic-coding.md)
- [Human in the Loop](../concepts/human-in-the-loop.md)
- [Context Engineering](../concepts/context-engineering.md)
- [Community-Driven Development](../concepts/community-driven-development.md)

## Entities

- [Cole Medin](../entities/people/cole-medin.md)
- [Anthropic](../entities/organizations/anthropic.md)
- [OpenAI](../entities/organizations/openai.md)
- [DeepSeek V3](../entities/tools/deepseek-v3.md)
- [Live Agent Studio](../entities/tools/live-agent-studio.md)
- [Pydantic AI](../entities/tools/pydantic-ai.md)
- [LangGraph](../entities/tools/langgraph.md)
- [Windsurf](../entities/tools/windsurf.md)
- [bolt.diy](../entities/tools/bolt-diy.md)
- [Supabase](../entities/tools/supabase.md)
- [n8n](../entities/tools/n8n.md)
- [Postgres](../entities/tools/postgres-pgvector.md)
- [Ollama](../entities/tools/ollama.md)
- [GitHub](../entities/tools/github.md)

## Key moments

- **[0:02:36]** Introduces the core motto: capabilities not tools
- **[0:04:32]** Story time: wasted a summer on advanced C++ (tool obsession) vs the next summer learning ML capabilities
- **[0:06:19]** Skill 1: AI agents will be the focus of 2025
- **[0:08:05]** Walks through Anthropic's 'Building Effective Agents' article and architecture patterns
- **[0:10:11]** Announces the $5,000 Live Agent Studio AI agent hackathon
- **[0:11:03]** Skill 2: reasoning LLMs and chain-of-thought / inference-time compute
- **[0:13:32]** Skill 3: local large language models as their own discipline
- **[0:15:39]** Skill 4: finding your AI tech stack; KISS and DRY
- **[0:20:02]** Cole shares his own full AI tech stack as an example
- **[0:23:35]** Skill 5: working with LLMs - prompting, coding assistants, human-in-the-loop, context windows
- **[0:26:58]** Closing: find an AI community; network is your net worth

## Transcript

[Raw transcript](../raw/how-to-dominate-with-ai-in-2025.md)
