Andrew Ng, founder of DeepLearning.AI, and Greg Hart, president and CEO of Coursera, are participating in a Q&A session at HumanX 2026 to address a critical challenge facing AI startups: the gap between understanding AI concepts and actually building production systems. The two education leaders will discuss how hands-on training and practical learning resources are becoming essential infrastructure for the next generation of AI builders .

Why Are Startup Teams Getting Stuck on AI Infrastructure Decisions?

Startup teams often know the business problem they want AI to solve, but struggle with the technical decisions required to build and deploy systems at scale. According to the HumanX 2026 agenda, these challenges include determining which graphics processing units (GPUs) to choose, assembling a working generative AI stack, and scaling infrastructure without incurring unpredictable costs or operational risks . For lean teams with limited resources, these decisions feel overwhelming because they require both deep technical knowledge and strategic thinking about long-term growth.

The conference addresses these pain points through multiple sessions designed to demystify the process. One masterclass walks teams through how to run generative AI workloads on Oracle Cloud Infrastructure (OCI) using OCI AI Accelerator Packs built on NVIDIA's full-stack AI platform, allowing startups to move from idea to live inference in days rather than months . This practical approach aligns with the philosophy that Ng and Hart have championed: making complex technology accessible to people without deep expertise.

What Real-World AI Problems Are Startups Trying to Solve?

The HumanX agenda highlights several concrete use cases that startups are building toward. These include AI copilots that assist users with specific tasks, retrieval-augmented generation (RAG) search systems that combine large language models with custom data sources, and route optimization algorithms that solve logistics problems . Understanding these specific applications helps teams make infrastructure decisions that actually match their needs rather than over-engineering or under-provisioning their systems.

The presence of both Ng and Hart at HumanX reflects a fundamental industry shift: education and skill-building are no longer afterthoughts but essential components of AI startup success. As the field moves from research to production, the ability to learn quickly and make informed technical decisions has become a competitive advantage.

How to Navigate AI Infrastructure Decisions for Your Startup

• Define Your Use Case First: Before selecting GPUs or cloud platforms, clearly identify the specific business problem your AI product will solve, such as AI copilots, retrieval-augmented generation search, or route optimization, to ensure your infrastructure choices align with actual needs .
• Evaluate Accelerator Packs vs. Custom Solutions: Understand when to use prebuilt generative AI stacks and one-click deployment options, which can reduce deployment time significantly, versus building infrastructure from scratch when your use case requires custom optimization .
• Plan for Scaling from the Start: Develop a clear mental model of how your costs and operational complexity will change as you scale from a few GPUs to larger clusters, ensuring you can grow without unexpected financial or technical surprises .
• Leverage Educational Resources: Take advantage of structured learning platforms and hands-on training to build internal expertise, reducing dependency on expensive external consultants and enabling faster decision-making within your team.

The HumanX 2026 conference, scheduled for April 6-9 in San Francisco, brings together founders, engineers, investors, and infrastructure leaders to discuss the practical implementation challenges of the AI era . Ng and Hart's Q&A session offers an opportunity for attendees to hear directly from two leaders who have shaped how millions of people learn AI and translate that knowledge into working products.

For startups navigating the AI landscape, the message from both education leaders is consistent: the teams that invest in understanding both the business and technical dimensions of AI will be better positioned to build products that work and scale sustainably. Education is no longer a luxury but a critical component of competitive advantage in the AI startup ecosystem.