Leveling Up with Eric Siu

Most businesses are still using AI the wrong way. They are stuck using ChatGPT like a search engine while the companies moving fastest are building end to end workflows, autonomous agents, and closed loop systems that compound over time.

In this video, I break down the four levels of AI adoption in business, why most teams fail with implementation, and how to actually build systems that increase revenue instead of creating more busy work. I also walk through real examples of how we use Hermes, OpenClaw, Slack, and specialized agents inside our company to handle strategy, analytics, ad creatives, workflows, and decision making.

If you want to understand how AI will actually change the way businesses operate over the next 12 months, this is the framework you need to see.

Chapters:

(00:00) Why Most Businesses Use AI Wrong

(00:22) The 4 Levels of AI Adoption

(01:03) Open Loops vs End to End Workflows

(02:31) The Power of Closed Loop Systems

(03:52) Why AI Adoption Is Failing in Companies

(05:30) Why Every Business Needs One Brain

(06:50) The Future Org Chart of AI Teams

(07:52) Real Examples of AI Agents at Work

(09:14) Build in 22 Minutes, Not 22 Weeks

(09:53) How I Use Hermes Inside Slack

(11:43) Creating Compounding Growth Loops

(12:48) Why Nobody Talks About This Yet

Most people are still using AI like it is 2023. They use ChatGPT like a search engine, ask random questions, and stop there.

In this video, I break down the three levels of AI usage from open loops to end to end workflows to fully closed loop systems that recursively improve themselves over time. I walk through practical examples including travel planning workflows, AI sales systems, autonomous agents, Slack based collaboration, YouTube content packaging, investment research, and how I personally use Hermes and OpenClaw agents every day.

I also show how AI agents can work together inside Slack, connect to tools like Google, Meta, SEO platforms, X, and internal systems, and become true thought partners instead of simple chatbots.

If you want to actually understand how to use AI for leverage instead of just experimentation, this is the framework.

Chapters:

(00:00) Why most people use AI the wrong way

(00:23) Open loops vs end to end workflows vs closed loops

(01:18) Building repeatable AI workflows

(02:44) How recursive self improving systems work

(04:18) Why autonomous agents matter

(05:02) How I use Hermes and OpenClaw daily

(06:00) Using AI for YouTube content and research

(06:45) AI investing research and thought partnership

(08:25) Running multi threaded workflows inside Slack

(09:11) Why closed loop systems are the future

Most companies are using AI completely wrong. They use ChatGPT in isolation, run random prompts, and wonder why nothing compounds.

In this video, I break down the exact Single Brain system we use to connect agents like OpenClaw, Hermes, and NemoClaw into one unified intelligence layer that helps teams move dramatically faster. We cover how these AI fleets plug into Slack, HubSpot, Salesforce, Google Search Console, analytics tools, ad accounts, and internal data systems to create a compounding workflow engine that actually generates revenue.

I also walk through real examples including AI generated ad creatives, automated reporting, scaling top performing campaigns into hundreds of variants, reducing operational costs by $500,000, and how one person with agents can outperform entire traditional teams.

If you want to understand where AI agents are actually heading and how businesses are using them to create leverage right now, this is the framework.

Chapters:

(00:00) Why most AI adoption fails

(02:06) Connecting all your business tools into one brain

(03:22) The AI org chart of the future

(05:20) Why most teams are still using AI wrong

(06:31) Human timelines no longer work

(07:10) Building ad creatives with AI agents

(08:06) Scaling campaigns into 200 variants automatically

(08:47) How $7,500 in tokens saved $500,000

(10:02) Why AI agents will replace traditional workflows

Here’s why most AI agent systems break once they touch real business operations.

The issue is not intelligence. The issue is control. Most companies are building disconnected prompts with no evaluation systems, no approval layers, and no recursive learning loops. That works for demos, but it falls apart when agents start touching production systems, ad spend, customer data, or outbound communication.

The better approach is treating agents like an operational command system. Hermes becomes the control tower that launches goals, evaluates outputs, routes approvals, stores learnings, and continuously improves future execution while humans stay in the loop for anything high risk.

In this video I break down how the AI optimization lab works, why recursive self improvement matters, how approval gates protect revenue and reputation, the difference between safe autonomy and dangerous autonomy, and how to structure agents that continuously move the business forward without creating operational risk.

Chapters:

(00:00) The real problem with AI agents

(00:54) AI optimization lab explained

(02:00) Hermes as the control tower

(03:26) Safe autonomy for businesses

(04:56) Why approval gates matter

(06:01) Human approval for risky actions

(07:42) Recursive self improvement loops

(09:20) Scaling autonomous systems

(10:31) Using Hermes to grow revenue faster

Here’s why Google’s new design.md standard could completely change how brands create content with AI agents.

Right now most brands exist in formats AI can’t consistently understand. Your landing pages, ads, decks, and creative assets are scattered everywhere with no persistent design memory. Google’s new design.md format changes that by giving agents a structured way to understand your visual identity and generate assets that actually stay on-brand.

In this video I break down how design.md works, why Google is trying to make it the default standard for AI-generated design, how we’re using it internally with agents, and why this becomes massively important for marketing teams trying to scale creative output without losing consistency.

Chapters:

(00:00) Why AI currently cannot “see” your brand

(00:22) Google’s new design.md standard explained

(01:06) Why Google wants to own the format

(01:37) Real examples using ClickFlow and Single Grain

(02:21) How agents generate branded assets automatically

(02:43) Why open standards matter more than lock-in

(03:23) The massive impact on marketing teams

(04:04) Sales decks and personalized design workflows

(05:01) The GitHub repo with reusable design systems

(05:24) Using inspiration from top-performing websites

(06:13) Why design.md could become the industry standard

(06:29) How revenue agents change creative production