Happy Friday, everyone, and welcome to another edition of the Future-Focused Live Q&A recap!
This week, I wanted to dig into a contentious topic dominating workplace discussions and social feeds right now: AI scores, digital detection tools, and the battle against “AI slop.”
It was sparked by Substack rolling out its partnership with Pangram Labs to assign an automated AI score to any post over 100 characters in an attempt to curb “claudefishing.” Just this week, LinkedIn introduced new reporting functions for suspected AI slop. When I surveyed thousands of leaders and professionals on this, the results were a dead-even 33/33/33 split: one-third loved the idea of AI scores, one-third hated it, and one-third were caught right in the middle.
The problem? Reaching for compliance, labeling, and algorithmic policing is an incomplete response that creates countless unintended side effects. It’s like inspecting someone’s grocery list to determine whether they’re a good cook instead of evaluating the actual meal.
Per usual, I addressed high-impact questions curated from you across three distinct perspectives: Senior Executives, Middle/Frontline Managers, and Individual Contributors.
With that, here’s a breakdown of the questions and my responses to them.
The AI Score & The Compliance Trap
Executive Lens: Brand Integrity vs. Scalability
The Challenge: C-suite leaders responsible for brand integrity and public trust can’t manually review every piece of content. Isn’t an automated AI score the only realistic, scalable filter to stop low-effort slop from damaging company reputation?
Detection scores aren’t inherently useless. They can serve a purpose and do a decent job catching zero-thought, prompt-dumped garbage. However, treating an algorithmic score as a conclusive determination of something’s value is dangerous. Use detection scores should serve purely as an initial flag to spark conversation or trigger secondary review, not as a replacement for human judgment and merit-based evaluation. If you’re attempting to automate 100% of your oversight, you’re eroding brand integrity anyway.
Manager Lens: Team Paranoia & Underground Behavior
The Challenge: Teams are growing anxious about being branded as “claudefishers” or copy-pasters for using AI to brainstorm outlines. How can managers stop a policing mindset from driving bad behavior underground?
Frontline managers need to serve as the primary shield for their teams. When upper leadership pushes compliance metrics, it’s important for managers to establish clear, transparent team processes. Lead with good-faith questions rather than interrogations. If a team member produces high-quality work with surgical AI assistance, defend them, the output, and the process rather than letting algorithmic hall monitors create an environment of fear.
Individual Contributor Lens: The Fear of Losing Credibility
The Challenge: Thoughtful creators who use AI surgically to refine research or structure cluttered thoughts are terrified that arbitrary percentage scores will brand them as “bots.” Should they just quit using AI tools altogether?
Please, do not run and hide or abandon tools that genuinely enhance the quality of your work. Use this wave of scrutiny as an opportunity to audit your process. If you can stand behind the merit, voice, and thinking of your final output, stand firm with confidence. When challenged, shift the conversation away from the tool score and force critics to speak directly to the merit of what you produced.
Human Agency vs. Digital Hall Monitors
Individual Contributor Lens: Penalizing Structured Thinkers
The Challenge: Why are clear, methodical, highly organized thinkers getting dinged by AI detectors just because their logical style happens to mimic algorithmic patterns?
While well-built detectors rarely flag purely original work at 100%, algorithmic detection tools naturally reward whatever style their creators programmed as “acceptable.” It’s important not to forget they’re also trained to detect a technology designed to mirror humans. The core issue isn’t just the detector software; it’s how we react to it. If we collectively decide that a high AI score is simply an invitation to have an open-faith check-in on merit, these tools completely lose their power to inflict psychological shaming.
Manager Lens: Inspecting the Kitchen vs. True Autonomy
The Challenge: If managers don’t use validation tools or inspect how work is being produced, aren’t they leaving the door wide open for unvetted, hallucinated junk to slip through under the banner of “autonomy”?
Inspecting and managing your kitchen is essential management. However, leaning on an automated AI score to do your job is an abdication of leadership. Relying on a tool score to tell you if work is good is a crutch. Set explicit standards for output quality, check the work on its substance, and educate your team on why public perception matters, rather than managing by digital surveillance.
Executive Lens: Balancing Full Agency with Organizational Risk
The Challenge: How can senior executives focus on outcomes and grant full employee agency while still mitigating major risks like IP leakage or widespread low-quality output?
Senior leaders can establish high-level operational boundaries without micromanaging execution. Setting a firm standard that public-facing releases or board decks must undergo strict human verification is completely reasonable. However, leaders must stop reacting out of outrage. Publicly blasting teams over minor AI usage feeds the exact outrage cycle that social algorithms prioritize. Focus on clear standards and real personal accountability.
Moving Beyond Labels: An Outcome-Driven Framework
Manager Lens: Finding Bandwidth for Deep Dialogue
The Challenge: Leading with curiosity and holding detailed follow-up discussions sounds great, but burnt-out managers simply don’t have the time. Where do we find the bandwidth?
Front-loading clarity always feels like more work in the short term, but skipping it costs exponentially more time down the road. When leaders fail to define clear outcomes up front, teams churn out chaotic output, leading to endless fire-drills. I promise that taking the time up front to align on expectations and ask probing questions eliminates hours of downstream rework.
Individual Contributor Lens: Standing Out Without a “100% Human” Stamp
The Challenge: If platforms drop AI scores, how can human creators stand out amidst an exploding ocean of generic AI slop without relying on “100% human” verification?
We need to drop the myth that anything is “100% human-created” in 2026. Almost everything is touched by digital systems, search engines, or modern tech tools. “No AI was used” is not a business strategy or a differentiator, and using it as a scapegoat for why work isn’t gaining traction is a trap. Differentiate through depth, domain expertise, original insights, and authentic relationships. Quality and merit speak for themselves.
Executive Lens: Real Accountability Amid Output Explosions
The Challenge: If we dismantle digital surveillance tools, what does executive accountability look like when output volume is exploding across the company?
Look in the mirror. Executive leadership sets the pace, priorities, and cultural expectations. If your teams are pumping out massive quantities of unvetted AI slop, you need to ask yourself: Are you rewarding raw volume over strategic depth? Have you given your workforce clear priorities, or are you just demanding “more” without defining what “better” looks like? Executive accountability starts with establishing clear direction and giving teams room to focus on high-value outcomes.
Concluding Thoughts
Alright, well, that’s a wrap for this week’s Q&A recap!
At the end of the day, slapping a scarlet-letter percentage score on a piece of writing doesn’t magically make the work better. If we want to move past the chaos of AI slop, we have to stop trying to build better digital prisons and get back to clear standards, outcome-based accountability, and genuine trust.
PS: Beta Testers Wanted
Speaking of scores (a different kind), I want to share something I’m really excited about. Unlike digital hall monitors that stamp a badge on content to judge whether it’s “bot or human,” I’ve been busy rebuilding my AI Effectiveness Rating (AER).
Version 2 is a wildly improved personal developmental tool that examines six core behavioral disciplines. It’s designed to give you a clear snapshot of how effectively you are leveraging AI, highlighting blind spots, drift, and high-impact growth opportunities.
I am officially opening up some licenses for people willing to provide feedback. If you or someone you know would like to be part of the beta group to test the assessment and provide feedback, send me a DM or email.
Thank you as always for the incredible questions, the pushback, and the lively discussions. Have a fantastic weekend.
With that, we’ll see you on the other side!
