Architecting the Future: Leveraging AI, Cloud, and Data for Business Success
summary
Vendor stacks ship M365/GitHub co‑pilots and an extensible AI Studio with about 1,600 models ready for enterprise extension; CRM platforms offer a proprietary Einstein One LLM with zero‑data‑leakage and strict data masking. Quantum hardware today is noisy at the few‑hundred qubit scale, needs error correction with likely millions of qubits for advantage, and is positioned as a cloud accelerator, not a desktop device. GPT family training runs measured in months, small LLMs can run on phones, and post‑quantum crypto plus strict model‑training controls are active defenses.
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chapters (20)
- 0:00Introduction
- 1:24Panelists' backgrounds
- 3:08Quantum Computing as architecture
- 4:35Microsoft's AI and cloud role
- 5:58Salesforce's data and AI focus
- 7:24Generative AI in customer apps
- 9:30Salesforce and cloud vendors
- 10:57Training AI models and Quantum
- 14:11Quantum Computing state and potential
- 16:15AI model size vs practical use
- 16:58AI accessibility and ethics
- 19:49Microsoft's responsible AI
- 20:09Salesforce's ethical AI design
- 21:55Quantum Computing ethics
- 24:46Fostering AI innovation
- 26:11AI education at Microsoft
- 27:56Future of AI and Quantum
- 29:42Audience Q&A on AI platforms
- 32:12Cloud maturity and AI impact
- 33:36Call to action
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