In the rapidly evolving landscape of artificial intelligence, companies are transitioning from initial excitement to demanding concrete results. According to Mike Krieger, co-founder of Instagram and current chief product officer at Anthropic, the era of AI FOMO is giving way to a more measured approach focused on success metrics and return on investment.
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The Shift From AI FOMO to Measurable Outcomes
Krieger, who joined Anthropic in 2024, has observed a significant transformation in how enterprises approach artificial intelligence adoption. “Two years ago, many AI tools didn’t have metrics attached to them at all,” he noted during the “Superhuman AI: Decoding the Future” podcast. “They were driven by this AI FOMO that was happening in the CIO suite.” This initial rush mirrored other technology sectors where companies feared being left behind, similar to how organizations scrambled during the renewable energy transition or reacted to major market movements like Microsoft’s 40% stock surge.
The current environment represents a maturation of the market, with companies now seeking tangible proof that their AI investments are delivering value. This shift reflects a broader pattern in enterprise technology adoption, where initial enthusiasm gives way to practical evaluation, much like what occurred during previous technological revolutions including the rise of cloud computing and mobile applications.
Daily Active Users: The Simplest Success Metric
Krieger emphasizes that daily active users provide one of the most straightforward indicators of an AI tool’s effectiveness. “I often get the question, how do I know if Claude Code is working for my organization?” he said. “I often ask people just to look at the daily active metrics because those don’t lie. People do not use tools over and over again every day if they’re not providing value.”
This focus on usage metrics has led to increased monitoring of which employees are adopting AI tools and which are falling behind. Major technology companies, including Meta, have responded by developing dashboards and even gamified systems to track AI usage patterns. The approach aligns with how successful platforms measure engagement, whether in social media or enterprise software environments.
Quantifying Productivity Gains Across Departments
While usage metrics provide initial validation, companies are increasingly seeking to measure specific productivity improvements. Krieger highlighted that for certain roles, such as technical support and legal departments, the metrics are relatively straightforward. “How much shorter was turnaround time on certain tasks?” serves as a clear benchmark for these functions.
However, Krieger acknowledges that measurement becomes more challenging in less structured environments. “When it gets fuzzy, it’s very hard to then evaluate, did it help?” This challenge reflects the broader difficulty in measuring knowledge work productivity, where outputs are often qualitative rather than quantitative.
Google provides a notable example of successful measurement, with CEO Sundar Pichai reporting that AI had created a 10% boost in engineering velocity. As detailed in subsequent explanations, Google arrived at this figure by measuring the increase in engineering capacity in hours per week generated by AI-powered tools. This type of concrete measurement provides a template for other organizations seeking to quantify their AI investments.
Strategic Evaluation Before Implementation
Krieger advises companies to ask fundamental questions before investing in new AI tools: “Is this a good product now, and is this a product that’s going to set up to succeed and scale?” This strategic approach helps organizations avoid the pitfall of adopting technology simply because competitors are doing so or due to industry pressure.
The evaluation process should consider both immediate functionality and long-term scalability. As Krieger noted, “The best products can be grounded in some kind of success metric or evaluation.” This principle applies across the AI ecosystem, from established players to emerging companies like QumulusAI raising $500M for infrastructure expansion and major cloud providers such as Oracle expanding multicloud offerings.
The Evolution of Enterprise AI Adoption
The current focus on metrics represents the natural evolution of enterprise technology adoption. Krieger’s perspective is informed by his experience building Instagram into a platform with widespread business use, giving him unique insight into how tools transition from novelty to necessity.
As companies move beyond the initial AI FOMO phase, they’re establishing more sophisticated evaluation frameworks. These include not just usage metrics but also qualitative assessments of how AI tools integrate into existing workflows and contribute to broader business objectives. The conversation has shifted from whether to implement AI to how to implement it effectively and measure its impact.
Practical Implementation and Continuous Evaluation
Successful AI implementation requires ongoing assessment rather than one-time deployment. Krieger’s observations suggest that companies should establish baseline metrics before implementation and track changes over time. This approach allows for continuous optimization and ensures that AI tools are delivering sustained value rather than temporary novelty.
The emphasis on practical evaluation reflects a broader trend toward data-driven decision making in enterprise technology. As organizations become more sophisticated in their AI strategies, they’re developing customized metrics that align with their specific operational needs and strategic objectives. This tailored approach ensures that AI investments support rather than disrupt core business functions.
For those seeking deeper insights into AI implementation strategies, additional perspectives are available through resources like the Superhuman AI podcast and other industry analysis that explore the practical challenges and opportunities in enterprise AI adoption.
