Podcast
Leveling Up with Eric Siu
Hosted by Eric Siu · EN
Leveling Up is a weekly interview series with entrepreneurs and marketers on the latest in business and marketing. Learn actionable strategies & tactics on how you can make your business grow and mistakes to avoid during your journey. Learn from individuals who have founded billion dollar companies to best-selling authors. Entrepreneur and host Eric Siu also shares insights and learnings along the way.
48episodes
Episodes
Newest firstAll episodes
The Next $1T Opportunity Isn’t SaaS
5d ago00:12:14Tap to summarizeEveryone is chasing AI software, but the biggest opportunity may actually be services. In this video, Eric explains why top investors are betting on services-as-software, how AI is reshaping agency and consulting business models, and why the future belongs to companies that sell outcomes instead of labor. He breaks down managed growth loops, AI-powered operating systems, and the new organizational structures that will separate winners from everyone else. If you're building an agency, consulting firm, service business, or AI startup, this video will change how you think about growth, valuation, and the next decade of opportunity. Chapters (00:00) Why Services Beat SaaS (01:13) The $1 Software vs $6 Services Opportunity (02:52) Why Managed Growth Loops Matter (04:49) Agents, Loops, and Human Judgment (06:43) How Single Brain Powers AI Service Businesses (07:22) The Services-as-Software Manifesto (08:41) The New AI-Native Org Chart (10:13) Building Outcome-Based Offers (11:13) Final Thoughts
Transcribe →How to Use AI to Grow Revenue in 2026
1w ago00:14:07Tap to summarizeMost 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
Transcribe →You’re Still Using AI Like It’s 2023
2w ago00:10:52Tap to summarizeMost 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
Transcribe →The Single Brain Setup That Makes Teams 100x Faster
2w ago00:11:22Tap to summarizeMost 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
Transcribe →How /goal Will Grow Your Revenues So Fast It's Unfair
3w ago00:12:04Tap to summarizeHere’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
Transcribe →The One File That Makes AI Actually Understand Your Brand (And Drive More Sales)
3w ago00:07:22Tap to summarizeHere’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
Transcribe →Hermes Vs OpenClaw - Which One Makes More Money?
4w ago00:08:11Tap to summarizeHere’s the real difference between OpenClaw and Hermes when it comes to actually making money with AI agents. OpenClaw has the bigger ecosystem, more integrations, more community support, and way more features. Hermes is newer, but it’s faster, more reliable, and learns alongside you over time through persistent memory and skill files. In practice, that means OpenClaw feels like the execution layer, while Hermes feels more like the brain. In this video I break down where each agent wins across reliability, security, features, and community, how we structure them inside our “single brain” system, why reliability matters more than features for business use cases, and the exact way we’re thinking about deploying agent fleets inside companies right now. Chapters: (00:00) OpenClaw vs Hermes overview (00:28) What OpenClaw already helped us achieve (01:05) Why Hermes feels more stable (01:23) The 4 categories that matter most (01:52) How our team uses agents inside Slack (02:25) Reliability problems with OpenClaw (03:14) Security tradeoffs and risks (04:23) Why OpenClaw still wins on community (05:05) Feature comparison between both agents (05:44) Why reliability matters most for business (06:07) Hermes as the “brain” and OpenClaw as execution (06:54) Final verdict on which agent wins today
Transcribe →OpenClaw Just Replaced My ENTIRE Cold Email Operation
4w ago00:11:47Tap to summarizeHere’s how one person can now run cold email infrastructure that used to require an entire team. Most outbound systems break because there are too many moving parts. You need lead sourcing, email verification, inbox warmup, campaign management, copywriting, optimization, and reporting all happening at once. In this video I show how agents inside a “single brain” system handle most of that work end-to-end while a human stays focused on judgment, strategy, and approvals. I also walk through how we’re using OpenClaw, Instantly, Whisper Flow, and recursive scoring systems to rewrite campaigns, manage infrastructure, QA sequences, and launch campaigns in parallel without needing multiple operators. Chapters (00:00) Why cold email used to require a full team (00:32) How the “single brain” system works (01:18) Reviewing Instantly campaign performance (02:09) AI rewriting and scoring email sequences (03:06) Why humans still need to stay in the loop (04:21) Incentives, personalization, and reply rates (05:41) Running multiple campaign workflows in parallel (06:28) Managing lead distribution and infrastructure (07:07) Reviewing campaigns inside Instantly (08:05) Fixing ICP targeting and send settings (09:01) Live feedback and campaign optimization (10:07) Why one person can now operate like a full outbound team (10:49) How companies are building “world brains”
Transcribe →Is It Game Over For OpenClaw
May 700:11:00Tap to summarizeHere’s the real state of OpenClaw right now. OpenClaw became a critical part of how our team operates, but over the last couple months the reliability has noticeably dropped. Messages fail, automations break, gateways hang, and teams start losing trust in the system when it stops responding consistently. In this video I walk through Peter Steinberger’s public apology, the exact issues we’re seeing inside Slack and Telegram, why reliability matters more than features, and how we’re thinking about Hermes vs OpenClaw moving forward. I also break down the “brain vs execution” model, why competition between the two is actually healthy, and why I still believe autonomous agents are the future despite the current issues. Chapters (00:00) Is it over for OpenClaw? (00:46) The reliability problems we’re seeing (02:08) Peter Steinberger’s apology (04:20) Why SSR matters (secure, stable, reliable) (05:05) The single brain + agent fleet setup (06:34) Real Slack failures inside our team (08:05) Telegram failures and broken responses (09:09) Hermes as the alternative (10:41) Brain vs execution model (12:03) Why OpenClaw still matters (13:34) Website deployed using OpenClaw (14:52) Final thoughts on the future of agents
Transcribe →This Happened 3 Times In 125 Years. AI Just Did It Again
May 400:07:12Tap to summarizeHere’s why the “AI will cause mass unemployment” narrative is probably wrong. Every major wave of technology has triggered the same fear, and every time it’s played out differently. AI doesn’t just replace jobs, it shifts them. It removes repetitive work, increases productivity, and creates entirely new roles that didn’t exist before. In this video I walk through real historical data from radiology, agriculture, spreadsheets, and ATMs to show how job displacement actually works, why demand often increases, and how AI acts as a multiplier rather than a replacement. Chapters (00:00) The mass unemployment narrative(00:22) Radiology example (AI vs jobs)(01:08) AI as a demand multiplier(02:06) Drivers and task vs job thinking(02:28) Agriculture automation (tractor era)(03:46) Spreadsheets and job evolution(05:25) ATM prediction vs reality(05:42) Creative destruction explained(06:36) Why AI likely creates more opportunity
Transcribe →