AI is not a new field, but its market size and user base are growing exponentially, creating significant business opportunities.
Most GenAI developer tools focus on the top of the funnel (writing code), creating bottlenecks in testing, operations, and monitoring.
Adopting GenAI introduces challenges like vendor complexity, moving beyond simple chatbots, and a lack of established best practices for AI systems.
Building robust GenAI requires moving from a model-centric to a system-centric view, encompassing orchestration, data, guardrails, and security.
Effective agentic systems rely on complex infrastructure for non-deterministic data flows and specialized hardware scheduling, underscoring the "garbage in, garbage out" principle.
AI systems introduce unique security risks like data poisoning and require specialized monitoring for performance, explainability, and model drift.
To address challenges like algorithmic bias, responsible AI principles must be embedded directly into the design of platforms and infrastructure.
AI maturity is a non-linear journey focused on time-to-value, creating new roles like the AI engineer and requiring careful consideration of when not to use GenAI.
25:27Maxim Salnikov
25:17Scott Hanselman
29:00Alex Soto
28:27PJ Hagerty
21:22Kapil Gupta
24:24Damir
35:16Ankit Patel
59:39Julian Joseph
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Jobs that call for the skills explored in this talk.
