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Greetings Decision Maker,
The White House trumpets a “baby bonus”—$5,000 wired days after delivery—to reverse America’s record-low 1.6 fertility rate, documented by the CDC’s National Vital Statistics Reports. In the same breath, it orders every federal employee back to the office five days a week. Stanford’s new “Working from Home in 2025” survey of 16,422 professionals upends that logic: women with children desire 2.66 remote days each week, higher than any other demographic. The administration vows to grow families while vaporizing the flexibility that makes new children feasible, creating a collision that risks empty cribs and hollow offices alike.
To learn more, check out this blog.
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Prefer video to text? See this video based on the blog: |
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“I thoroughly enjoyed your presentation at the SHRM conference at Tarleton State University. It is very obvious that you are highly intelligent, but you brought the information down to a level we could all understand. I really liked that you walked around and looked everyone in the eye. That makes the information stick that much better. The five question card will go with me to teach Principles of Management. It looks like it would work well in every-day situations."
Jodie Dearing, Graduate Program Manager in the College of Business Administration at Tarleton State University |
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I want to share a quick story from the trenches of AI implementation that taught me a valuable lesson about flexibility and thinking differently when tech fights back. I was building an AI agent using Microsoft’s Copilot Studio to help insurance adjusters generate standardized claims letters based on policy language. It worked well for single-policy scenarios—think one form, one letter. But when we tried to scale it up to handle two policies at once, things got weird. Microsoft recently introduced a major update focused on “relevancy,” which includes a strict post-generation validator that wipes any output unless it precisely matches the policy language and the user’s prompt. The problem? If only one policy mentioned something like “wind damage to fences” while the other didn’t, the AI would generate a solid response—then immediately erase it. I tried adjusting the prompt wording and backend settings, but nothing worked. I considered using Power Automate and AI Builder as a workaround, but that would’ve added unnecessary complexity for our users, since part of my goal is to teach the claims adjusters to actually own and manage the tool. What ended up working was reframing the problem. I split the process into two steps—one per policy—so each could pass validation independently. It’s not flawless, maybe getting both prompts 70-80% of the time, but usually at least one of the two prompts works. The key lesson? When AI tools are rigid, don’t just push harder—step back, rethink the structure, and find a way to work with the system’s constraints, not against them. |
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Would love to get your feedback on what you found most useful about this edition of the “Wise Decision Maker Guide” - simply reply to this email.
Decisively Yours,
Dr. Gleb
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Dr. Gleb Tsipursky
CEO of Disaster Avoidance Experts
PS: Are we connected on LinkedIn? If not, please add me.
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Disaster Avoidance Experts is a social enterprise dedicated to promoting science-based truth-seeking and wise decision-making. All profits are donated to Intentional Insights, an educational 501(c)(3) nonprofit organization, and its Pro-Truth Pledge project.
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