Speak at The Fifth Elephant 2026 Annual Conference

AI coding assistants are everywhere. Organizations are rolling them out at scale, developers are using them daily, and productivity gains are widely celebrated.

But there is a question few teams are asking:

What happens when AI becomes the primary way developers learn?

Recent research from Anthropic found that developers using AI assistance while learning a new programming library scored significantly worse on conceptual understanding, debugging, and code comprehension. Some AI usage patterns improved task completion, but at the cost of learning; others preserved both productivity and skill development.

In this talk, I will explore what these findings mean for engineering leaders building AI-native organizations. We will examine the emerging trade-off between short-term productivity and long-term capability, the risks of cognitive offloading, and why traditional metrics such as velocity and task completion may no longer tell the full story.

Most importantly, I will present a practical framework for maximizing the benefits of AI while preserving the human skills—judgment, debugging, system thinking, and technical depth—that remain critical in the age of AI.

Key Takeaways

• Why higher productivity does not necessarily translate into higher competence.
• The hidden risks of cognitive offloading in AI-assisted software development.
• The six AI usage patterns identified in recent research and which ones preserve learning outcomes.
• Why debugging, code comprehension, and conceptual understanding may become the most valuable engineering skills in the AI era.
• How engineering leaders should rethink onboarding, training, performance management, and career development in AI-native organizations.
• A practical framework for balancing AI-assisted productivity with long-term skill formation.

Who Should Attend

• Engineering Leaders, Startup Founders
• CTOs and VPs of Engineering
• AI Leaders and Heads of AI
• Engineering Managers
• Software Engineers
• Developer Productivity Teams
• Learning & Development Leaders

Why This Matters

As AI increasingly writes code, the bottleneck shifts from code generation to code understanding. Organizations that optimize solely for short-term productivity may unknowingly weaken the very human capabilities required to supervise, debug, and improve AI-generated systems. This talk explores how to avoid that trap and build teams that become both more productive and more capable in the age of AI.

Speaker Bio

Anuj Gupta helps Organizations convert AI aspirations into concrete AI systems that deliver outcomes (in the capacity of Head of AI).

• Built the flagship AI system for a YC startup that:
  • Impressed Sam Altman & Vinod Khosla
  • Showcased by OpenAI in their flagship events
  • Helped YC startup secure funding from Khosla Ventures
  • Vinod Khosla spoke about this system, addressing Hon’le PM at the AI CEO roundtable at the recently concluded India AI summit, 2026
• Built core AI systems at one of India’s earliest AI startup, acquired for its AI capabilities by Nasdaq listed Unicorn.
• Published a major book in AI with Oreilly, US; endorsed by top names in AI including those from CMU, UCSD, DeepMind, Google AI, flagship YC startups like Airbnb
• Brings 20+ yrs of expericnce of building 50+ AI systems across startups & Fortune 50; serving first decade as AI researcher & later decade as senior AI Leader

More about him

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