On AIBiz, host Avik digs into the real-world use of AI inside large, aging codebases—and why onboarding developers is getting harder, not easier. Guest Francesco Hayes explains where AI genuinely helps (documentation, test scaffolding, diff review, pattern discovery) and where it regularly fails (context-window blind spots, cascade regressions, over-reliance during onboarding). We cover risk in high-stakes domains like healthcare and govtech, the true cost of AI infrastructure, and why strong human review and architectural thinking still decide outcomes. If you’re leading teams through post-MVP cleanup, scaling CMS components, or hiring developers who lean too hard on prompts, this conversation gives you a pragmatic playbook to ship safely and faster—without breaking the media pipeline on deploy day.

About the Guest :

Francesco Hayes builds high-impact web products for mission-driven organizations across healthcare, nonprofits, and private equity. Known for translating messy, post-MVP realities into scalable, documented systems, he leads teams through refactors, cost optimization, and sustainable delivery.

Key Takeaways :

• Use AI where the blast radius is low: documentation, unit test generation, code comments, and summarizing modules before deep dives.

• In big repos, AI can fix one bug and quietly create another. Guardrails: smaller PRs, strict diff review, and component-level scopes.

• For onboarding, have new devs ship first tickets without AI to learn the architecture, then layer AI as an accelerator—not a crutch.

• Treat AI as junior-dev level help. Senior engineers still decide boundaries, data flow, and failure modes across services.

• In regulated domains (healthcare, government), mandate human review and security checks; tolerance for AI-introduced errors is near zero.

• Real interviews should mirror production: build a feature tied to your stack, not generic algorithm drills that AI can autocomplete.

• Expect hiring asymmetry: some teams need fewer devs due to AI leverage; others on the bleeding edge need more engineers to integrate safely.

• Most developer time isn’t typing code—it’s problem framing, infra, feedback loops, and testing. AI frees time for that work when guided well.

• Cost and energy constraints matter. Model choice, context strategy, and privacy/security posture must fit your budget and risk.

• Bottom line: AI augments; it doesn’t replace deep contextual judgment, especially in multi-vendor, multi-tenant systems.

Connect with the Guest

• X (Twitter): @FrancescoHayes_

• Best first touch: DM on X; he’s centralizing updates there.

Want to be a guest on Healthy Mind, Healthy Life? DM on PM - Send me a message on PodMatch
DM Me Here: https://www.podmatch.com/hostdetailpreview/avik

Disclaimer: This video is for educational and informational purposes only. The views expressed are the personal opinions of the guest and do not reflect the views of the host or Healthy Mind By Avik™️. We do not intend to harm, defame, or discredit any person, organization, brand, product, country, or profession mentioned. All third-party media used remain the property of their respective owners and are used under fair use for informational purposes. By watching, you acknowledge and accept this disclaimer.

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