The Dime

Understand This Before You Use AI To Build Software ft. Chris Guthrie

Episode Summary

AI has made software easier than ever to build. That does not mean every internal tool should become the backbone of your business. In this episode, Bryan sits down with Chris Guthrie to break down the growing temptation inside cannabis companies to vibe code internal tools, dashboards, CRMs, ERPs, and operational systems in the name of saving money. The problem is not the first version. The problem is what happens two months later, when a field does not map, the workflow breaks, the person who built it is unavailable, and a production team is now relying on code nobody fully understands. Chris explains the difference between a useful AI-built prototype and an enterprise-grade system that can survive real operators, messy data, security needs, compliance, multiple departments, M&A diligence, and future FDA-style requirements. This conversation covers why descriptive data does not create predictive insight, why Google Sheets to vibe coding can become a path to failure, and why the real AI opportunity starts after the business has clean data, clean processes, and a system strong enough to trust. This episode covers: The weekend build that fails Descriptive data will not predict Clean books before M&A

Episode Notes

AI has made software easier than ever to build. That does not mean every internal tool should become the backbone of your business.

In this episode, Bryan sits down with Chris Guthrie to break down the growing temptation inside cannabis companies to vibe code internal tools, dashboards, CRMs, ERPs, and operational systems in the name of saving money.

The problem is not the first version. The problem is what happens two months later, when a field does not map, the workflow breaks, the person who built it is unavailable, and a production team is now relying on code nobody fully understands.

Chris explains the difference between a useful AI-built prototype and an enterprise-grade system that can survive real operators, messy data, security needs, compliance, multiple departments, M&A diligence, and future FDA-style requirements.

This conversation covers why descriptive data does not create predictive insight, why Google Sheets to vibe coding can become a path to failure, and why the real AI opportunity starts after the business has clean data, clean processes, and a system strong enough to trust.

This episode covers:

Chapters

 

00:00 The Rise of AI in Software Development

03:07 Challenges of Internal Software Development

06:09 Understanding What 'Works' in Software

09:10 The Risks of Vibe Coding

12:05 The Importance of Established Infrastructure

14:58 Cost Considerations in Software Development

17:56 Predictive Insights and Data Utilization

21:13 Defining Core Business Focus

23:17 The Challenges of AI Implementation

27:00 Understanding AI's Limitations

28:58 The Importance of Data Governance

31:11 Navigating Change Management in Cannabis

35:51 Preparing for Industry Evolution

40:09 The ROI of ERP Systems

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