In the documentary Led Zeppelin: Dazed and Confused, tour manager Richard Cole discusses drummer John Bonham’s idiosyncratic hotel room behavior:
Bonham smashed everything. He even got the security to help him smash the pool table because he couldn’t do it on his own. Before we were checking out [the hotel manager] came up… He’s looking at all the damage. And he said to Bonzo, kind of looking down his nose very smugly, “oh, you left the mirror.” Bonzo said “oh did I? I’m sorry about that,” and smashed it right in front of him… When we went to pay the bill, the [hotel] manager was seething. And [band manager] Peter said “we paid for the damage, what’s the big deal?” And he said “it’s not that. It’s just that you guys can do what you want. I work in this hotel. I hate this. I’d love to do what you do.” Peter said “pick a room and give me the bill.” And he did. The guy went down, threw everything out the window, smashed the windows and gave us the bill. Peter got the cash out, paid him, and that was it.
Doing what you love can be destructive. Although this lesson may not have been absorbed by Bonzo or room trash confrères Keith Moon (The Who) and Joe Walsh (Eagles) (i.e., untimely deaths for the first two, hit song for Walsh: “I live in hotels, tear out the walls / I have accountants pay for it all”), the hotel manager probably learned it that day, even if Led Zeppelin did pay the bill. Right there with the hotel manager is Internet bad boy and bon vivant Marc Andreessen, who said this in a recent interview:
Don’t follow your passion. Seriously. Don’t follow your passion. Your passion is likely more dumb and useless than anything else. Your passion should be your hobby, not your work. Do it in your spare time. Instead… find the hottest, most vibrant part of the economy you can and figure out how you can contribute best and most.
The question of what young people should be studying has never been more important. As students physically re-enter schools, many after 18 months of attention-span-depleting Zoom school, and most after plenty of time for quiet soul searching, taking advantage of an immersive educational environment to pursue a passion is more tempting than ever. The Great Return occurs in the midst of a mass migration out of the arts and humanities. Bachelor’s degrees in philosophy, history, English, and languages are down over 30% in two decades, providing an awful job market for thousands of newly minted Ph.D.s who followed their passions and a tense backdrop for a new higher education workplace dramedy on Netflix.
So where are students going? Driven by unaffordable tuition and unsupportable student loan debt, they’re flooding into majors with a patina of employment connectivity. Health majors, homeland security and law enforcement have been the biggest winners. But pick any pre-professional major besides education (!) and you’ll find students who, a generation earlier, would have been relitigating the causes of the Civil War or deciphering Derrida.
When Marc Andreessen says don’t follow your passion, what he’s really saying is study STEM – the hottest, most vibrant part of the economy. Of the 25 top paying majors, 23 are scientific and technical. Andreessen is particularly keen on engineering and computer science. In his manifesto of last spring, It’s Time to Build, he accuses America of failing to build enough housing, factories, transportation, energy, hospitals, schools, and – of course – technology. Solving this problem requires more investment, but also more talent capable of building with bricks and clicks. Engineering and computer science enrollments are up in the past decade, but not as much as these other areas, which – with fewer “weed out” courses – are perceived by students as easier and more accessible.
The good news is that with accelerating digital transformation of the economy, the skills actually required to build are easier to learn. What once required mastering multiple coding languages can now be done with low-code or no-code development platforms. Many good entry-level build-with-clicks jobs now center on platforms like Salesforce or Epic, the leading electronic medical record system. And while few academic institutions are oriented to select and train on in-demand software and SaaS platforms, these skills can be acquired relatively quickly, in a last-mile training format. That is, as long as students are adequately prepared...
What does it mean to be prepared for the last mile? Achieve’s portfolio companies recruiting candidates for new apprenticeship pathways are figuring this out as they go. But the common denominator appears to be, in the parlance of Lumina Foundation’s DQP, quantitative fluency – meaning the ability to assess and manipulate quantitative evidence in evaluating claims – with a strong dash of statistics and data analytics.
Quantitative fluency, statistics, and data skills aren’t only required for building what we need, but also maintaining what we have. Quantitative iliteracy is hobbling America, as – in the words of Apoorva Mandavilli of the New York Times – we increasingly “live with science as it unfolds in real time.” In international rankings, American teens rank poorly (behind Poland and Slovenia) and our slip is more than showing as we try to close the door on Covid. Beyond bogus election fraud claims, being able to distinguish anecdote (or simply made up stuff) from relevant data has become a matter of life and death. If only mask mandate protesters could read scientific publications. And if only my college classmate Alex Berenson weren’t the The Pandemic’s Wrongest Man and looked at U.S. hospitalization and ICU data ahead of his anti-vaccination tweets pushing forsythia (whoops, I mean Substack subscriptions and self-published screeds). Although, I must say, reunions are likely to be more bonding now that our class has a villain who may be responsible for the deaths of thousands of Americans. I’ll let you know how the next reunion goes, but based on the anti-Alex relationships reestablished via social media, having a class villain seems worthwhile if you can arrange it (without the casualties).
Meanwhile, the science we thought would unfold in geologic time is also happening in real time. Disasters of biblical proportions are no longer primarily associated with the old testament or Ghostbusters (dogs and cats, living together), but rather this week’s weather. At the end of a calamitous summer, I took my boys camping in Zion National Park where an eager young ranger delivered a fine presentation on climate change and was heckled by some Trumper. When I tried to engage the heckler on science, her response was “you’re a weak American,” as though wrestling with data or the limits of our planet’s atmosphere makes someone weak or un-American. (I’ll bet she doesn’t consider herself a weak Christian when confronted with the awesome power of God.)
As I noted in last year’s discourse on Alex Berenson (planning on making it an annual thing until he stops), one of the benefits of quantitative fluency is understanding and being able to live with ambiguity. Few things are 100% certain. So in the absence of complete information, having a handle on probability is key to judgment and decision making. At the same time, 1% uncertainty is not the same as flipping a coin. Understanding probability and accepting ambiguity lead to a level of humility and deference to expertise rarely found among angry anti-mask/vax protesters, Zion visitors who are somehow 100% certain that our current lifestyle won’t lead to the roasting or parboiling of our great-grandchildren, or economics major Alex Berenson (see e.g., the tweet that finally got him suspended, hopefully permanently).
With few exceptions (Wellesley, CUNY’s Lehman College), these last-mile building blocks are viewed as the responsibility of STEM departments. But because colleges and universities provide multiple options for students to circumvent distributional requirements and rigorous STEM coursework (i.e., non-selective schools seeking completion rates that aren’t totally embarrasing), every year millions of students are granted degrees while remaining quantitatively illiterate. That means not being prepared for last-mile training or responsible citizenship (i.e., truly “weak” Americans).
Students shouldn’t have a choice whether to learn these skills. Quantitative fluency ought to be baked into every program and course. As UT Austin professor of history Steven Mintz noted last year, “humanities departments should do much more… to prepare students in such high-demand fields as computational thinking, data mining, data visualization, geospatial analysis, time series network analysis, provenance, data privacy, 3-D digital reconstructions and simulation modeling.” As fluency requires being able to assess quantitative relationships across different contexts, it’s more effective if students can build quantitative muscles in history, literature, political science, and psychology. Moreover, developing quantitative fluency isn’t best accomplished via lectures or discussion groups, but by doing. Students need to grapple with data, formulas, and models in order to build these skills.
The good news is that it’s never been easier due to the same force requiring tens of millions of additional quantitatively fluent citizen-workers: digital transformation. Ready-made data sets across disciplines and powerful analytics and visualization platforms like Tableau are widely available and free to colleges and students. In addition, apps like MobLab (games and experiments for economics and social sciences) and Stukent (simulation for business analytics) continue to come on the market. So the primary barrier to cross-curricular quantitative fluency initiatives is not availability of tools, but rather the willingness and quantitative fluency of non-STEM faculty (overcoming which will be a true test of institutional leadership).
Within STEM programs themselves, platforms like Chem101 (visualization scaffolds and easy-to-use drawing tools), Labflow (labs), and Phet (simulations for physics, chemistry, math, earth science, and biology) allow students to spend less time receiving (reading, listening) and more time doing. If we want students to think scientifically, they need to spend more time developing hypotheses, testing, observing, analyzing, and refining. Engineering and computer science are particularly well suited for these tools and should become higher education’s home for learning by doing.
All signs point to a long bull market in quantitative skill building in STEM and across the curriculum. But while colleges and universities must lead the way, once they establish new tech-enabled models for quantitative fluency, the primary use case should be high school. Many of America’s biggest problems are the direct byproduct of a system of K-12 education that, like last month’s tuna, is well past its sell-by date. It's not entirely the fault of K-12 districts and unions; technology has raised the bar. Ignorance wasn't an existential problem before technology made it contagious. But equating a high school diploma with quantitative fluency would do wonders for American democracy, not to mention productivity, economic growth, and reducing inequality. And given the massive increase in K-12 acceptance of new edtech tools over the past 18 months, there’s no time like the present.
It's likely that room trash kings like John Bonham and Keith Moon had little to no ability to estimate the cost of the damage they caused. A half century later, quantitative illiteracy has proven even less affordable than these hotel bills. While I don’t expect a scientific revolution, the good news is that new digital and edtech tools have us on the cusp of a revolution in scientific teaching. Like Bonzo and Moon, colleges and universities that make no effort to use edtech to embed quantitative skill building into every program of study may be heading for an early demise.