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At a glance
WHAT IT’S REALLY ABOUT
Enterprises are buying AI fast, but can’t prove productivity gains
- Enterprise AI budgets are growing rapidly, but leaders widely suspect a majority of projects are wasting money because they cannot measure outcomes beyond licenses purchased.
- Measuring AI value resembles early ad tech attribution: without shared infrastructure for measurement and governance, spend increases faster than confidence in results.
- “Productivity baseline” is the core problem—companies struggle to define outputs, avoid Goodhart’s Law metric-gaming, and separate usage from actual business value.
- Adoption is constrained by employee anxiety and training gaps, so enabling “safe” AI use and practical workflow integration is as important as model capability.
- AI’s labor impact is framed as augmentation and competitive advantage rather than mass unemployment, with winners using AI to grow faster rather than simply cut headcount.
IDEAS WORTH REMEMBERING
5 ideasAI has an ROI visibility crisis, not just a capabilities problem.
Companies can easily buy tools, but many executives report they only track “how much we bought,” not whether AI improved outcomes—creating a repeat of early internet advertising’s attribution fog.
The productivity baseline is missing, so “more productive” is often meaningless.
If AI lets a lawyer finish in 4 hours what used to take 8, the employee benefits immediately, but the company only benefits if outputs increase, responsiveness improves, or staffing/work allocation changes—none of which is captured automatically today.
Usage data must be paired with outcome measures; surveys alone are unreliable.
Self-reported productivity is biased (definition ambiguity, desire to please, fear of looking bad), and it ignores the reality that some licensed users never log in; behavioral usage telemetry is the necessary first layer.
Goodhart’s Law is a real risk in AI performance metrics.
If companies promote a metric (e.g., messages sent, lines of code, dollars spent on coding assistants) into a target, employees can game it—so metrics should guide internal diagnosis more than direct employee scoring.
A practical measurement approach triangulates three signals: usage, output/impact, and time/“tonnage.”
The proposed path is to correlate heavy vs. light tool usage with department-specific productivity indicators and with aggregate time/throughput—evaluated at group levels to avoid privacy and noise at the individual level.
WORDS WORTH SAVING
5 quotes85% of the companies we talked to said they really believe they only have the next 18 months to either become a leader or fall behind.
— Russ Fradin
Every board meeting I go in for my other four metrics, I have some report of how are we doing against those reports, and on AI, all I have is the amount of stuff we bought.
— Russ Fradin
When a measure becomes a target, it is no longer accurate as a measure.
— Alex Rampell
I'm sure that was very cool for him, but that's absurd. That's an absurd way to hope people adopt world-changing technology.
— Russ Fradin
Cursor has taken mediocre engineers and made them good, but it's taking amazing engineers and made them gods.
— Russ Fradin
SPEAKERS
Russ Fradin
guestEntrepreneur and operator focused on enterprise software and measuring AI-driven productivity/adoption.
Alex Rampell
hosta16z host and investor who interviews founders and operators about technology and business.
High quality AI-generated summary created from speaker-labeled transcript.
