In this episode of New Research, we talk about what enterprise AI actually looks like once it hits production. Benjamin White is joined by Pawel Czech (Surge) for a deep, practical conversation with Jorge Torres, CEO of MindsDB. Jorge has been building AI systems inside real companies since long before “chat with your data” became the default pitch and he brings a grounded view shaped by messy data, legacy systems, risk, and accountability.

We unpack why “having lots of data” can be as useless as having none, why moving intelligence to the data layer matters more than moving data into new tools, and how enterprises should think about the progression from discovery to analytics to automation without blowing past their own risk limits. Along the way, we cover why SQL still matters, where most companies get stuck, how to design AI systems people can actually trust, and why open source has been a strategic advantage rather than a philosophical choice.

What we cover:

• Why enterprise AI keeps failing after the demo
• “Don’t move the data, move the intelligence” and why that abstraction scales
• Predictions vs generation and why chat isn’t the endgame
• Where search ends and analytics really begins
• When automation is a mistake, not a milestone
• Open source, auditability, and human accountability in AI systems
• What will feel obvious about enterprise AI in five years that most teams are still missing today

If you care about AI that ships, survives contact with real organisations, and delivers value without creating silent failure modes, this one’s for you.

Subscribe to New Research for upcoming episodes, and check out MindsDB’s open source repo and community to go deeper.

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In this episode of New Research, Benjamin White and Pawel Czech are joined by Peter Knez, a veteran of global finance who has operated at the very core of institutional capital.

Formerly Global Co-Chief Investment Officer at BlackRock, Peter helped scale financial products to trillions in assets under management. Today, as Co-Founder and Chairman of TEAL, he is applying that hard-earned systems knowledge to AI, blockchain infrastructure, and tokenized real-world assets.

This conversation goes beyond surface-level fintech narratives to examine what is actually changing inside financial services. The discussion explores where disruption truly begins, whether in products, infrastructure, or decision-making itself. Peter and Pawel unpack why AI represents a fundamentally different shift from prior automation waves, how probabilistic systems alter capital allocation and judgment, and what tokenization genuinely unlocks in terms of liquidity, programmability, and global participation.

The episode also confronts the hard truths Web3 and AI builders often underestimate: trust, governance, regulation, and operational resilience. As financial systems become more autonomous and onchain, the conversation turns to where trust is sourced, how power may shift geographically, and what builders, operators, and investors should focus on now to remain relevant in the next decade of finance.

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In this episode of New Research, Benjamin White and Pawel Czech sit down with Rakshit Choudhary, CEO of Deriv, to unpack what it actually means to run an AI-native financial business at global scale.

Serving millions of users across 100+ countries, Deriv operates under constant pressure: real-time execution, multilingual support, fraud prevention, and regulatory compliance, all running 24/7. This is not AI as a demo or a feature, but AI as infrastructure.

Rakshit walks through Deriv’s transition from experimentation to board-level AI strategy, where machine intelligence is applied selectively across operations, product design, compliance, and decision-making. The conversation explores where AI outperforms humans today, where it still fails, how internal teams and roles are changing, and what Deriv is building now to remain competitive through 2030.

The episode closes with a look at Deriv’s AI hackathon and how the company is opening its doors to builders, operators, and talent who want to work on real problems with real impact.

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