Marcelo Calbucci has been building tech for over 30 years. He’s a serial entrepreneur, multi patent holder, former engineering leader at Microsoft and Amazon, and someone known for having bold, contrarian takes on where AI is actually heading.

In this episode, Marcelo sits down with Ravi Belani to challenge some of the biggest assumptions people have about AI right now.

They dig into:

• Why layoffs and job freezes might have nothing to do with AI

• Why AI isn’t replacing software engineers anytime soon

• Where AI is being massively overused

• The areas where AI is actually underused and full of opportunity

• Why founders need to stop outsourcing strategy to LLMs

• How to use AI to disconfirm your ideas instead of chasing confirmation bias

• Why the next big wave of opportunities may come from “multiplayer AI”

• What roles AI is likely to replace, and which roles will remain essential

• Why adaptability will matter more than anything else in the next decade

Marcelo also shares which startup opportunities he’d pursue today, how he thinks teams will collaborate with AI agents in the future, and the one piece of advice he’d give founders in 6 words: We’re entering the world of adaptability.

If you want fresh, honest, and thought provoking perspectives on AI and the future of work, this conversation is a must watch.

Amr Awadallah has seen multiple waves of hype in tech and helped build real companies through it. He co founded Cloudera, took it public, watched it get acquired for 5.3 billion dollars, and is now building Vectara, a platform that helps enterprises ship AI apps that are accurate, safe and actually usable in production.

In this episode, Amr joins Alchemist Accelerator founder and CEO Ravi Belani to unpack a striking finding from an MIT report. An estimated 95 percent of enterprise AI projects are failing.

They dig into why that is happening and what to do differently:

• The real reasons enterprise AI fails in production
  

• Why “go do AI” from the board is not a strategy
  

• How fake exploration budgets create fake success stories
  

• The 3 whys every founder and buyer should test
  

• • Why do anything

  • Why now

  • Why this vendor
    

• How to tell a real business need from AI theater
  

• Why passion for the problem, not the tech, drives grit
  

• How cofounders, early hires and investors make or break your company
  

• What Vectara is building as an “operating system” for AI agents
  

• Why context, security, accuracy and cost control matter more than demos
  

Amr also shares raw, founder level advice. From firing fast in the first 50 hires, to choosing investors who have actually been in the trenches, to his simple test for whether you really love the problem you are working on.

If you care about building AI products that survive past the pilot stage, this conversation is a must listen.

Dr. Shelby Heinecke, Senior AI Researcher at Salesforce, joins Ravi Belani to explain why the future of AI will not belong only to giant models with hundreds of billions of parameters.

Shelby makes the case for small language models: compact systems with only a few billion parameters that can run faster, cost less, protect privacy, and still perform at a very high level when they are trained well on focused tasks.

In this episode, they dig into:

• Why small models are a different tool, not a weaker version of large models
  

• How fine tuned small models can beat much larger models on specific agentic tasks
  

• Where small models shine most: privacy, speed, cost to serve and on device use cases
  

• How Salesforce built “Tiny Giant,” a 1B parameter model that outperforms much larger models on selected tasks
  

• What really matters in training: data quality, workflows and trajectory style datasets
  

• How synthetic data, noise and guardrails help make models more robust in the real world
  

• Why founders should look closely at on device AI and domain specific small models
  

Shelby also shares practical advice for founders who want to build in the small model space, and closes with a simple takeaway: do not underestimate small models.

If you care about AI agents, privacy, edge computing or future startup opportunities, this conversation will give you a lot to think about.