Is the traditional SaaS model officially dead ? On this episode of BILLIONS, I’m sitting down with Matthew Fitzpatrick, the man Fortune 500 CEOs called when they didn’t know what to do with AI.

Matthew walked away from one of the most prestigious roles in tech, leading 1 000 engineers at McKinsey’s QuantumBlack Labs to lead Invisible Technologies.

Invisible is the "invisible" engine behind the AI revolution.

They don't just build software; they provide the RLHF (Reinforcement Learning from Human Feedback) and the data that trains the models the entire world is building on.

With $100M raised at a $2B+ valuation, Matthew is proving that the future isn't in selling tools, but in selling outcomes.

In this masterclass, we break down:

• The McKinsey Exit: Why a top AI leader "jumped ship" for a $2B startup.
• The Death of SaaS: Why "Outcome-based pricing" is replacing the subscription model.
• The Enterprise Gap: Why 90% of companies are failing to get AI into production.
• The Scaling Laws: The truth about data bottlenecks and the future of AI training.
• Process as Code: How Invisible integrates human intelligence with AI to solve "impossible" problems.

TIMELINE :

00:00 The data bottleneck: Why Enterprise AI is currently "stuck"

01:01 Why McKinsey’s AI chief left to lead a $2B unicorn

02:33 The "Four Platforms": How Invisible actually works

05:58 SaaS vs. Outcomes: The pricing model of the future

09:19 Why the "AI Bubble" reality check is coming

15:12 The "Capability Gap" holding back the Fortune 500

22:15 RLHF & Data: Building the workforce behind the major models

31:42 "Process is Code": The new architecture for billion-dollar companies

41:10 Matthew’s advice for founders: Don't just build a "wrapper"

48:20 The future of the "Invisible" empire

REFERENCES :

• Mary Meeker

• Elon Musk Étude MIT Sloan

• Étude NBER (National Bureau of Economic Research)

• Article Bloomberg

• McKinsey & Company 

• Quantum Black 

• Invisible Technologies 

• SwissGear 

• Y Combinator

• WeCP (We Create Problems)

• Databricks 

• Snowflake

• Jevons paradox 

• Reinforcement learning from human feedback (RLHF)

• Chain-of-thought reasoning 

• Revolut