june 2, 2022

Every Now and Then...

Jonny White

Every now and then the right person in the right place does the right thing, and we all benefit.


Marie Curie.



Al Green.

Mr. Rogers.


Neil DeGrasse Tyson.

They have utterly different talents and contribute in utterly different ways.

While these folks are famous, often the right person in the right place does the right thing and only some of us see it. A wonderful kindergarten teacher. A fantastic mom. An office manager who keeps everyone happy and organized with a giant smile on his face, genuinely calling it another day in paradise and helping make it so. 

What if we could double the number right people in the right place for them doing the right thing for them? What if we could create a tenfold increase? What about a hundredfold? How about if we could platform it?

It’s coming. It can be measured and it can be done

I know it sounds hippie-dippy to think about finding the right vibe, but this is a piece of tech. A data-based system. Consider what other systems have done. 

At some point, Jimmy Wales said, “What if we could get all the world’s encyclopedic content onto one free website with crowd-based contributions?”

And Tesla and Edison said, “What if we generate an electricity grid system so that people can have continuous access to that power?”

I apologize for being grandiose. The purpose is to get you thinking about systems with interacting components, so that I can introduce mine. Here we go:

Imagine a student trying to adapt to college. They feel lost and uncertain. They know there’s lots of potential to grow, but there are thousands of things they could do (join a club? study? budget? sleep? socialize? go to the writing center?), hundreds of stressors, and they don’t know where to start.

What if we…

  1. got a half-decent mathematical picture of who that student is. (“questionnaire”)
  2. used that mathematical picture to recommend actions to them that should work for who they are. (“recommendations”)
  3. had the student rate each action so that the system knew and could share WHAT WORKS FOR WHOM. (“ratings”)
  4. repeated this endlessly to improve the mathematical pictures and the recommendations. (“system”)

Maybe cool? Still fuzzy? Let's go deeper. 


Via validated questions, we find out the following about our student… 

  • where and what they study, 
  • what year they’re in, 
  • sleep, food, and exercise habits,
  • social comfort and connectedness,
  • engagement on campus,
  • personality like are they anxious? are they extraverted? are they conscientious?
  • living situation,
  • demographics like where they’re from and who in their family attended college.

It’s not a complete picture. Not even close. But it’s half decent. Scientists can predict a lot (tooth brushing, voting, marital success) to some degree of certainty with a lot less. All that matters is some degree of certainty because we’re just giving the student options for actions. 


We surface “plans” in the same way as dating apps surface matches. A card pops up explaining that there’s a small local scholarship open for women in STEM (and the user happens to be a female biology major who is worried about money). Add that plan. A card pops up detailing how to join an intramural sports team (and the user happens to feel isolated and not be exercising). Dismiss that plan. Too intimidating, for now. A card pops up recommending a website where you can find applied biology internships. Dismiss. A card pops up recommending a calendaring system (and the user happens to be a bit disorganized). Add. A card pops up with the schedule for gym tours and recommends just hopping on a spin bike for 30 minutes. Add that plan. We might not do it… but we might. 


As the student completes the steps in a plan, a little card with five stars pops up and is dismissed by tapping a star. Each step in each plan now has a rating. That rating (as well as the add/dismiss choices and the time spent on each plan) maps the mathematical profile of the user to each of the plans. All of the user maps together form a mathematical model that predicts, with some degree of accuracy, what plans will work best for each new user who comes in and answers some portion of the questionnaire.


This system isn’t perfect. Nor is it magical. It’s a recommendation engine, and like a Spotify playlist or Netflix, it will surface some hits and some misses. But unlike Spotify or Netflix, the initial recommendations are based on a mathematical profile, rather than just what things the student liked in the past. And unlike Spotify or Netflix, the user doesn’t have to listen to a song or watch a movie to provide a rating. They just have to glance at a suggested plan and dismiss or add with one gesture. 

The idea is that no one has to be lost. If a student answers some questions and chooses some recommendations, they might not be doing the perfect thing for them, but they will be doing one of the best things. And if they do one of the best things for them—for who they are as a human—every day or so, before long they’re likely to become quite the formidable human. 

The idea is that not everyone should grow in the same direction. We all have different roots and we find ourselves in different soils, so why shouldn’t we all have different branches? If being a lawyer suits someone, let’s show them how others like them became good and happy lawyers. If being a community organizer suits someone, let’s show them how others became good and happy community organizers. 

All the data about how particular individuals managed to thrive doesn’t need to be lost anymore All that past data—on how billions of people best found (and failed to find) their way—is pretty much lost, minus some books and second-hand accounts. But now we can easily collect the data while benefitting from it. It’s time to harness it to help people. 

The idea isn’t to make life easy. There’s too much to be done and humans don’t stop when a system makes life easy, anyway. Humans just take on harder and more complex challenges. This system give people supports, information, and challenges that suit who they are, maximizing the student’s engagement, contributions, and individuality. 

The system is starting to work. It’s been used for student success. For employment readiness. For mental health. There are plans for fitness and for new moms. We’re running paid trials with universities and colleges. If you’d like to see this at your campus, get in touch. 

If you’d like to discuss a different kind of relationship, for a different vertical or partnership or on the business or funding side of things, we’re happy to talk about it. In fact, we’re almost always happy to talk about whatever you’re good at and whatever works for you, because that’s probably where you’re at your best. 

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