Are engineering leaders asking the wrong questions when deciding what to build?
In this episode of Futureproof, Xano CEO Prakash Chandran talks with Chris Horn, SVP of Operations at Deriv, about what it takes to build AI inside a regulated, global software environment. Chris explains the difference between prototypes and proofs-of-concept, why data architecture is the real unlock, and how Deriv used a Shark-Tank-like model to introduce AI into internal operations. Together, they explore the mindset shift required for AI-native development - and why the most important question isn’t “Can we build it?” but “Should we build it?”
Topics covered include:
- Prototype vs. PoC: Why technical feasibility matters less than solving a real problem.
- AI as product work: The critical role of discovery, KPIs, and iteration in AI projects.
- Data as the foundation: How Deriv built a medallion architecture to get ready for AI.
- Internal AI first: Why customer-facing AI wasn’t the starting point (and what worked instead).
- Upskilling at scale: Building an AI-native culture through curiosity, training, and incentives.
- 00:00 TI-99 and Early Curiosity
Chris reflects on teaching himself to code at age 11 on a TI-99 and discovering the excitement of making software come alive.
- 05:00 The First SMS & Scale Lessons
He describes working on the team that sent the world’s first SMS and what building global telecom systems taught him about scalability and bottlenecks.
- 08:00 Transition to Fintech
Chris shares how he moved from telecom into fintech, and why Deriv values leaders who bring experience from different regulated industries.
- 12:00 What Deriv Is
Chris explains Deriv’s business model as a global online broker and outlines the role of reliability, latency, and compliance in their trading platforms.
- 15:00 Starting AI Internally
He discusses why Deriv began its AI journey inside internal operations before touching the customer experience.
- 18:00 Building with a Product Mindset
Chris describes how Deriv evaluates AI ideas using product thinking, sets clear KPIs, iterates quickly, and kills projects that don’t deliver results.
- 22:00 The Data Layer
He explains how Deriv built a medallion data architecture to support AI, and why real-time, well-structured data is essential to every successful application.
- 26:00 Upskilling and Culture
Chris shares how Deriv encourages employees to learn AI through training resources and hands-on experimentation.
- 32:00 Hiring AI-Native Talent
He describes what Deriv looks for in AI hires, including curiosity, adaptability, communication skills, and a consultative, product-oriented mindset.
- 37:00 The Future: AI + Low-Code
Chris explains why he believes the future of app development will combine modular AI with disciplined low-code tools, rather than pure vibe-coding.
- 43:00 Final Lessons
Chris closes by encouraging leaders to adopt a product mindset, stay customer-obsessed, and measure AI by the real impact it creates.