Confused by AI jargon and unsure which tools actually move the needle for your business?

We break down the real differences between traditional algorithms, large language models (LLMs), and agents — including agentic AI — and give practical guidance leaders can use now.

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Topics Covered in This Episode:

Choosing AI: Algorithms vs. Agents
Understanding AI Models and Agents
Using Conditional Statements in AI
Importance of Data in AI Training
Risk Factors in Agentic AI Projects
Innovation through AI Experimentation
Evaluating AI for Business Solutions

Timestamps:
00:00 AWS AI Leader Departs Amid Talent War

03:43 Meta Wins Copyright Lawsuit

07:47 Choosing AI: Short or Long Term?

12:58 Agentic AI: Dynamic Decision Models

16:12 "Demanding Data-Driven Precision in Business"

20:08 "Agentic AI: Adoption and Risks"

22:05 Startup Challenges Amidst Tech Giants

24:36 Balancing Innovation and Routine

27:25 AGI: Future of Work and Survival

Keywords:
AI algorithms, Large Language Models, LLMs, Agents, Agentic AI, Multi agentic AI, Amazon Web Services, AWS, Vazhi Philemon, Gen AI efforts, Amazon Bedrock, talent wars in tech, OpenAI, Google, Meta, Copyright lawsuit, AI training, Sarah Silverman, Llama, Fair use in AI, Anthropic, AI deep research model, API, Webhooks, MCP, Code interpreter, Keymaker, Data labeling, Training datasets, Computer vision models, Block out time to experiment, Decision-making, If else conditional statements, Data-driven approach, AGI, Teleporting, Innovation in AI, Experiment with AI, Business leaders, Performance improvements, Sustainable business models, Corporate blade.

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