AI agents are getting a lot more capable, but are they actually making work easier yet?
Craig and Colin compare their latest experiments with GrokBot, Hermes, OpenClaw, Claude Code, Codex, and other AI tools, including what works, what breaks, and where agents are genuinely reducing cognitive load.
They dig into open vs. closed systems, shared company agents, local AI, model costs, and the idea of an AI chief of staff that keeps founders focused.
Plus, Colin shares how he's using AI to overhaul a 15-year-old WordPress site, and they explore what the next generation of AI-powered websites and marketing tools might look like.
Highlights from Craig and Colin’s conversation:
- Open vs. closed AI agents for running a business
- Why Craig chose Hermes as Castos' shared company agent
- The security challenge of giving AI agents real power
- Why AI should reduce cognitive load, not create more work
- How Colin uses AI for CRM, task management, and daily planning
- Claude Code vs. Codex, Cursor, and other AI development tools
- Why expensive AI models aren't always the best choice
- The case for running AI locally or privately
- How cheap AI access encourages founders to experiment
- Can an AI chief of staff actually keep you focused?
- Using AI to overhaul a 15-year-old content site
- How tiny AI-built tools could replace traditional lead magnets
Resources and Links from This Episode:
Chapters
- (00:00:00) - Intro and topic setup: bots and agents
- (00:01:23) - Open vs closed agent platforms
- (00:03:35) - Why choosing Hermes for a shared team agent
- (00:07:07) - Permissioning and security challenges with Hermes
- (00:09:08) - Vision for AI teammates across a company
- (00:10:44) - Personal use: Grokbot vs Claude code workflows
- (00:12:53) - CRM, task management, and cognitive load reduction
- (00:16:09) - Mobile workflows, Notion, and GitHub syncing
- (00:19:06) - App preferences: Claude vs Codex vs Cursor vs Antigravity
- (00:22:00) - Right-sizing models and cost discipline
- (00:22:19) - Golf video workflow and downgrading models to save cost
- (00:25:33) - Local and private AI compute considerations
- (00:29:35) - Enterprise API costs, subsidies, and sovereignty concerns
- (00:32:35) - Value of experimentation under usage limits
- (00:33:56) - Chief of staff model and multi-agent orchestration
- (00:37:59) - Chief of staff for managing priorities and stale projects
- (00:41:25) - Old-school business plans teaser
- (00:42:01) - Renovating lawns4u.com with AI agents
- (00:45:00) - WordPress vs modern stacks (Astro, Next.js) discussion
- (00:47:33) - Building tools and lead magnets beyond WordPress
- (00:50:15) - Wrap up and future topics
