What You've Been Criticized For, AI Now Needs
AI hallucinates. Its training data is biased. The instinct you've been penalized for catches both.
The instinct that gets women called slow, careful, too focused on the context is the same instinct that catches what AI cannot.
If you’ve ever been told you ask too many questions, that you deliberate too long, are too careful or too focused on relationships and context that “aren’t really relevant,” this article is for you. (I suspect that’s just about every one of us.)
Last week, Sumaiya Shrabony published a piece in Code Like a Girl about the jagged frontier of AI — the invisible, shifting line between what AI does brilliantly and what it does confidently that’s completely wrong.
Her assertion: the instinct to verify before you ship, to ask “is this actually true?” before forwarding the polished thing upward, is not a liability. In an AI-saturated organization, it’s the highest-leverage skill nobody has recognized and given us credit for.
I left a comment about hallucinations. Specifically, about a pattern La Claude (my name for Claude.ai) described to me:
“AI hallucinates most confidently in domains where you know enough to follow along but not enough to catch the errors.
Full expertise catches it.
Full ignorance makes you cautious.
The middle zone — where you’re informed but not expert — is where confident wrong answers slip through most easily.”
Sumaiya called this “the piece I wish I had written.” She said it described the jagged frontier from the inside.
It did. And here’s why.
The Instinct You’ve Been Punished For
The reason women catch what others miss isn’t random. There’s research behind it. It’s not conclusive enough to describe every individual (you know yourself better than any study does) but it’s representative enough to name a pattern.
Neuroscience has identified meaningful differences in how the default mode network — the brain system associated with associative thinking, making connections across domains, generating ideas — is organized and activated. These differences track with gender. The DMN is precisely the network engaged when we hold context, notice what doesn’t fit, pull on the thread that seems unrelated but isn’t.1
In other words: the cognitive orientation that gets called “relational” - bringing context, asking the second question, noticing how the pieces connect - has roots in how brains are wired, not just how women are socialized.
We’ve been told to strip it out. Present just the facts. Get to the point. Stop asking.
We have been repeatedly asked to disable the function that now matters most.
Penalized for being slow.2 Penalized for being right.3
What AI Cannot Fix for Itself
AI has two failure modes that no prompt can engineer away.
Hallucination. AI produces confident, polished, plausible output that is simply wrong — invented citations, fabricated specifics, conclusions that fit the shape of the answer without fitting the facts. This is not a bug being patched. It is structural to how large language models work.
Embedded bias. Ground truth: AI has bias baked into its foundation. AI was trained on a world documented primarily by men, weighted toward male-coded authority, built to reflect a baseline that treats that authority as neutral and universal. Its output doesn’t have to hallucinate to mislead. It misleads, “accurately” and without self-examination, reproducing exactly the assumptions that built the glass ceiling, e.g. about who is qualified, who leads, the path for advancement and what leadership looks like.
Both failures require the same correction: a mind that holds the surrounding context, that asks whether the output fits with everything else it knows, that refuses to accept confidence as a proxy for truth and that takes time to tell AI to go back and “prove it.”
That is the relational mind doing exactly what it does. The time is a benefit not a bug.
They have been criticizing our processes for decades. Now they’re calling us slow for running the check that the quality of their decisions depends on. That is not a flaw in us. It is a flaw in how they are reading the situation.
This is one piece of a larger argument I’m making in Built Without You - that AI is hardening the glass ceiling in ways you need to understand in order to guard against. You can explore that argument, and sample chapters, here.
For now: go read Sumaiya’s piece: “Most Program Managers Use AI Like a Search Box. The Job is Knowing Which Tasks to Hand It.” . Read my comment below it. And the next time someone tells you you’re not on point, remember that you actually are!
And for a related article:
Lead ON!
Susan
If you’re following all the advice and it still feels like something’s in the way, I’m telling you, you are not the problem. You are working inside a system that’s withholding something from you. And now AI is making it permanent.
I’m here to offer what that system and embedded AI won’t and can’t: the business, financial and strategic acumen that keeps you visible and opens doors. I do that through the complete 6-part ecosystem that is being built for you here.
❤️ Please like, share or restack this post — it’s the best way to help other women find it
📘 Explore the ecosystem:
Your Business Savvy Coach - the only AI tool that coaches you on developing and demonstrating your business savvy as you engage with it on projects, pitches and priorities. Its Coaching Integrity layer guards against the embedded bias that generic AI tools carry in unchecked.
Built Without You: How AI is Hardening the Glass Ceiling and What to Do About It
The AI Intercept Pack - tool for working efficiently with AI while guarding against its embedded biases. (coming soon)
They Never Told You There Was a Horse in the Barn: The Business Savvy They Never Taught You. Why It Changes How You Work and How Far You Go
Be Business Savvy Course - self-paced, online course equipping you with the business, financial and strategic acumen skills you need to succeed and outrun the ways AI is hardening the glass ceiling.
Be Business Savvy articles here on Substack.
Ryali, S., Zhang, Y., de Los Angeles, C., Supekar, K. & Menon, V. (2024). Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization. Proceedings of the National Academy of Sciences, 121(9), e2310012121. https://doi.org/10.1073/pnas.2310012121
Textio. (2024). Language bias in performance feedback. textio.com/feedback-bias-2024 — Analysis of 23,000 performance reviews across 250 U.S. workplaces. Key finding: 88% of high-performing women receive personality-based feedback in reviews, compared to 12% of high-performing men; women receive 22% more personality feedback than men overall.
Chawla, N., Spoelma, T. M., Kwon, S.-H., Gabriel, A. S., Ellis, A. P. J. & Wu, W. (2026). Understanding the intersection of gender and cognitive ability on interpersonal outcomes: A multistudy investigation. Journal of Applied Psychology. Advance online publication. https://doi.org/10.1037/apl0001391