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Atmrix

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Atmrix

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SC

Scott Clark

05/23/25

@ a16z

Many organizations focus on high-level performance metrics, which can mask undesired behaviors in AI systems, leading to a lack of robustness.

Video

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Building AI Systems You Can Trust

@ a16z

05/23/25

Related Takeaways

SC

Scott Clark

05/23/25

@ a16z

Organizations must adapt their performance metrics to account for potential biases and undesired behaviors in AI systems over time.

SC

Scott Clark

05/23/25

@ a16z

The challenge of ensuring AI systems do not exhibit undesired behaviors is more complex than simply achieving high performance metrics.

DS

David Silver

04/11/25

@ Google DeepMind

We must be cautious about how we define metrics for AI systems to avoid unintended consequences, such as optimizing for the wrong outcomes.

SC

Scott Clark

05/23/25

@ a16z

Behavioral analysis of AI systems should include not just performance metrics but also characteristics like toxicity, reading level, and response time.

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a16z Cast

05/23/25

@ a16z

Developers should focus on the holistic behavior of AI applications at scale, rather than just performance checks on small datasets.

SC

Scott Clark

05/23/25

@ a16z

Enterprises must ensure that AI applications align with their business values and goals, rather than solely focusing on performance metrics.

YC

Y Combinator Cast

06/18/25

@ Y Combinator

The AI industry suffers from a lack of rigorous evaluations and tests that demonstrate the frontier of model capabilities.

MK

Mike Krieger

06/05/25

@ Lenny's Podcast

Product metrics for AI should focus on the depth of engagement rather than just frequency, as meaningful interactions may not always show up in traditional metrics.

DP

Dwarkesh Patel Cast

04/04/25

@ Dwarkesh Patel

Regulatory measures could backfire if they lead labs to train AI to avoid revealing harmful intentions.