Alejandro Saucedo
The State of GenAI & Machine Learning in 2025
#1about 5 minutes
The long history and rapid market growth of AI
AI is not a new field, but its market size and user base are growing exponentially, creating significant business opportunities.
#2about 4 minutes
Analyzing the developer productivity funnel for GenAI tools
Most GenAI developer tools focus on the top of the funnel (writing code), creating bottlenecks in testing, operations, and monitoring.
#3about 2 minutes
Overcoming the key challenges of building with GenAI
Adopting GenAI introduces challenges like vendor complexity, moving beyond simple chatbots, and a lack of established best practices for AI systems.
#4about 5 minutes
Deconstructing the modern agentic systems stack
Building robust GenAI requires moving from a model-centric to a system-centric view, encompassing orchestration, data, guardrails, and security.
#5about 3 minutes
Agentic infrastructure and the critical role of data
Effective agentic systems rely on complex infrastructure for non-deterministic data flows and specialized hardware scheduling, underscoring the "garbage in, garbage out" principle.
#6about 2 minutes
New security vulnerabilities and monitoring for AI systems
AI systems introduce unique security risks like data poisoning and require specialized monitoring for performance, explainability, and model drift.
#7about 1 minute
Implementing responsible AI principles by design
To address challenges like algorithmic bias, responsible AI principles must be embedded directly into the design of platforms and infrastructure.
#8about 3 minutes
AI maturity, new roles, and appropriate use cases
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.
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