Metatrend #1: Abundance: Food, Energy & Education

“Go to college, get a great job” is no longer the valid paradigm. And this is putting higher education in a tailspin.

Here’s the data you should know:

• Americans calling college “very important” crashed from 75% to 35% in 15 years, while “not too important” quintupled to 24%
• Tuition is up 899% since 1983, saddling 42 million borrowers with $1.8 trillion in debt—second only to mortgages
• College graduates now make up one-third of the long-term unemployed, up from one-fifth a decade ago
• Job postings requiring degrees dropped 6% since 2019

You’re paying a quarter million dollars for a private university education that increasingly guarantees nothing. The credential that used to open doors is now locking people out.

So, what do you (or your kids) do about it?

1/ You need to credential yourself differently now. College used to be the way to signal competence. That era is over. Today, credentialing means building a portfolio that demonstrates value: GitHub repositories showing what you’ve shipped, a consulting practice proving you solve real problems, a YouTube channel teaching what you know. The act of getting accepted by MIT still matters. The four years spent there increasingly don’t.

2/ Top ten schools remain exceptions (and still surprisingly credible). If you’re getting into MIT, Harvard, Stanford, or their peers, the brand still carries weight. These institutions run on massive endowments: their investment returns contribute over twice as much to their budgets as all tuition combined. But schools ranked 40 to 400? They’re facing an existential crisis as perceived value craters.

3/ Employers are waking up to degrees being worthless. Companies are hiring without degree requirements at accelerating rates. Why? Because the curriculum moves too slowly. As one MIT administrator admitted: “We can build a nuclear reactor on campus faster than we can change this curriculum.” By the time you graduate, what you learned freshman year is obsolete.

4/ Master one skill: growing revenue, and you’ll always have work. Most companies care about exactly one thing: increasing revenue. If you can demonstrably grow top-line numbers, degrees become irrelevant. Show me you drove $2M in new sales or built a product that acquired 100K users. That’s infinitely more valuable than a transcript.

5/ The best educator in the world will be AI-driven. AI tutors will provide one-on-one adaptive instruction in any subject, any language, at near-zero cost. Programs using AI-assisted learning already show students learning 5-10 times faster than traditional classrooms. The university model of one professor lecturing 300 students is dead… it just doesn’t know it yet.

6/ Trade school is now another option. As Jensen Huang, CEO of Nvidia, recently said: “If you’re an electrician, you’re a plumber, a carpenter—we’re going to need hundreds of thousands of them to build all of these factories.” These professions today command immediate jobs and salaries between $100,000 and $150,000 per year without the extraordinary debt of college.

Here’s the opportunity: The credential economy is being rebuilt from scratch. The old gatekeepers are losing their monopoly. This creates a massive opening for those who demonstrate value directly through their work product, build networks through entrepreneurship, and learn faster using AI than any traditional program could teach.

The future belongs to the self-credentialed.

The defining issue of the week was antitrust enforcement. In a landmark decision in a US district court in the Google search remedies trial that will undoubtedly shape the future of the internet, a federal judge opted against forcing Google to spin off its Chrome browser but barred it from striking exclusive agreements to promote its search engine.

Tech Policy Press associate editor Cristiano Lima-Strong reported on the decision, including in a piece looking at how Judge Amit Mehta’s considerations around artificial intelligence shaped the ruling, which heavily emphasized the role generative AI tools like ChatGPT could play in the future of online search and information retrieval.

There’s a lot more to consider this week, starting with two round-ups:

• Catch up on what happened in US tech policy in August with a roundup from Freedman Consulting’s Rachel Lau and J.J. Tolentino and Tech Policy Press managing editor Ben Lennett, including coverage of the federal push for AI in gov’t, the potential harms of AI chatbots to children, and developments in Congress.

• Check out another installment of the Global Digital Policy Roundup for August 2025 from the experts at Digital Policy Alert. Maria Buza, Aishwarya Vaithyanathan and Tommaso Giardini highlight tech policy developments in content moderation, artificial intelligence, competition, and data governance.

What was once dismissed as speculation, machines that do science, cells programmed like software, consciousness measured and perhaps replicated, is now edging from possibility to probability. The question is no longer whether the old certainties will fracture, but how soon, and how profoundly.

This cluster of near-horizon advances, in fields once considered separate provinces, physics, biology, mathematics, computer science, cognitive science, are now edging toward one another, like tectonic plates. Their collision threatens to remake the continent of knowledge.

Today, AI systems are proposing mathematical conjectures, predicting protein interactions, designing drugs, and in some cases devising experiments beyond the imagination of their human handlers. “The end of theory” was once a glib New Yorker headline; today, it begins to feel literal. Science is building a machine that can do science. The implications are not only practical (shorter drug pipelines, new materials) but existential. When intelligence ceases to be a uniquely human monopoly, the Enlightenment’s quiet assumption, that reason is our species’ birthright, collapses.

Now we have Master Plan Part IV.

In this latest installment, Tesla is barely a car company anymore, and Elon no longer cares about the climate crisis. Sustainable energy is out. “Sustainable Abundance” is in. (Thanks, Ezra and Derek.) Now Tesla “make[s] physical products at scale and at a low cost with the goal of making life better for everyone.” It “build[s] the products and services that bring AI into the physical world.” Sure.

None of this tells us anything about the actual future of Tesla as a trillion-dollar producer of material objects. The point of these master plans is to make the stock number go up, or at least to prevent the number from trending down. And that’s because we judge publicly traded companies based on their stock performance, which is practically untethered from their net revenues.

The most noteworthy part of Master Plan IV isn’t the rosy Optimus/AI predictions. It’s the date. Elon went a decade between the first two plans, seven years between the second and third plans, and just two years between the third and fourth.

If Tesla’s underlying business was going well, Elon Musk wouldn’t need to ring this particular bell so frequently. By 2031, I estimate he will be releasing a new Master Plan every eleven hours. It’s the beginning of an exponential curve! Imagine a future of unlimited PR jargon.

Here is how unbundling the American university would work.

When students are accepted to any university, they would henceforth have a choice between (at least) two prices. The first price—let’s call it the White Lotus option—would offer the bundled service which they currently need to purchase in order to enroll. On top of access to their academic coursework, it would provide them with a room in a dorm, with a meal plan, with access to the university gym, and with all of the other amenities to which college students have become accustomed.

The second price—let’s call it the I’m Here to Learn option—would be based solely on the cost of providing a stellar education. It would allow students to complete their undergraduate education, including access to lectures and seminars and laboratories, but exclude the other amenities which have come to be such a big part of the university experience. Students would themselves be responsible for securing housing, for feeding themselves, and for figuring out their leisure activities.1

Are colleges, as nearly all of them claim, actually serious about making their institutions “financially accessible,” about catering to a “diverse set of needs,” and about creating greater “ideological diversity”? Well, here’s the logical first step towards realizing all of these goals.

What is a World Foundational Model?

World foundation models (WFMs) are neural networks that simulate real-world environments as videos and predict accurate outcomes based on text, image, or video input.

These LLMs will accelerate how robotics can learn to do tasks and work together in a variety of ways. Companies like Google, Nvidia, Meta, Physical AI and Skild AI, among others, are working on this. Everyone wants to build the “software brain” for the future of robotics.

In a short span of time, companies like Nvidia, Ai2, Google and others released their WFMs with relevance to robotics simulation.