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Already Here · August 6 · 43 min

AI Consultant: You Are Using AI Wrong

Thanh Pham does not begin an AI setup with a long list of tools. He begins with one repeated task, one result a person can recognize, and one clear reason to use it again. In this episode, Michael talks with Thanh about helping executives move from curiosity to practical use. Thanh demonstrates small workflows with obvious value, then adds more capability only when the person understands and trusts the result. He also explains the difference between local and cloud AI. A local setup runs on a computer the person controls, which can provide more control over where data is handled. A cloud setup sends work to computing services run elsewhere, which may offer different capabilities but requires a deliberate decision about what information leaves the machine. In this episode: • Why Thanh teaches one useful skill before introducing a larger system • How an automatic meeting briefing gathers email history, messages, and public research 30 minutes before a call • How lead enrichment adds public details to a contact so a business can judge whether that lead is a good fit • What an AI “agent” means here: software that can use approved tools and complete a sequence of steps • Why Thanh gives one assistant responsibility for email, calendar, and meetings, while another handles research and project work • The practical difference between running AI on your own computer and using a cloud service • Thanh’s 10-80-10 model: define the job, let the system do the work, then review the result yourself Thanh’s approach is easy to remember: choose one repeated task, make the first result immediately useful, and keep the final review with a person. His examples show that practical AI adoption depends less on presenting every possible tool than on giving each assistant a clear job and giving the person a result worth trusting. About Already Here: Michael meets people already living with personal AI in everyday life. We look at the tools, but the real story is what changes in three places: how work is assigned, how decisions are made, and where people spend their attention. Subscribe for more conversations with people already living with personal AI. Then comment with your biggest takeaway and the first repeated task you’d like to test. Follow Thanh Pham on X: https://x.com/runsonai Follow Michael Galpert on X: https://x.com/msg Official links mentioned in this episode: https://openclaw.ai/ https://github.com/NousResearch/hermes-agent https://chatgpt.com/ https://claude.com/ https://openai.com/codex/

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show notes

Thanh Pham does not begin an AI setup with a long list of tools. He begins with one repeated task, one result a person can recognize, and one clear reason to use it again.


In this episode, Michael talks with Thanh about helping executives move from curiosity to practical use. Thanh demonstrates small workflows with obvious value, then adds more capability only when the person understands and trusts the result.


He also explains the difference between local and cloud AI. A local setup runs on a computer the person controls, which can provide more control over where data is handled. A cloud setup sends work to computing services run elsewhere, which may offer different capabilities but requires a deliberate decision about what information leaves the machine.


In this episode:

• Why Thanh teaches one useful skill before introducing a larger system

• How an automatic meeting briefing gathers email history, messages, and public research 30 minutes before a call

• How lead enrichment adds public details to a contact so a business can judge whether that lead is a good fit

• What an AI “agent” means here: software that can use approved tools and complete a sequence of steps

• Why Thanh gives one assistant responsibility for email, calendar, and meetings, while another handles research and project work

• The practical difference between running AI on your own computer and using a cloud service

• Thanh’s 10-80-10 model: define the job, let the system do the work, then review the result yourself


Thanh’s approach is easy to remember: choose one repeated task, make the first result immediately useful, and keep the final review with a person. His examples show that practical AI adoption depends less on presenting every possible tool than on giving each assistant a clear job and giving the person a result worth trusting.


About Already Here: Michael meets people already living with personal AI in everyday life. We look at the tools, but the real story is what changes in three places: how work is assigned, how decisions are made, and where people spend their attention.


Subscribe for more conversations with people already living with personal AI. Then comment with your biggest takeaway and the first repeated task you’d like to test.


Follow Thanh Pham on X: https://x.com/runsonai

Follow Michael Galpert on X: https://x.com/msg

Official links mentioned in this episode:

https://openclaw.ai/

https://github.com/NousResearch/hermes-agent

https://chatgpt.com/

https://claude.com/

https://openai.com/codex/