Packy McCormick's latest dispatch from Not Boring doesn't just report on a week of news; it constructs a compelling narrative about the collision of high-stakes ambition, artificial intelligence, and institutional inertia. The piece is notable for its refusal to treat these as separate silos, instead weaving a story where a disgraced tech titan returns to build a physical computing empire while AI models simultaneously dismantle century-old mathematical proofs. For the busy professional, the value lies not in the headlines themselves, but in McCormick's framing of these events as a fundamental shift in how we build, discover, and fund the future.
The Return of the Physical Computer
McCormick centers the first section on Travis Kalanick's re-emergence with Atoms, a conglomerate that aims to treat physical industries like software. The author highlights the sheer scale of the $1.7 billion investment led by Andreessen Horowitz, noting that this is less a standard startup round and more a reconciliation of past rivalries. "On many levels, this round is a bit of unfinished business," Kalanick wrote, referencing the 2011 deal that almost brought a16z into Uber before falling apart. McCormick argues that this capital is fueling a vision where manufacturing becomes the CPU, real estate is storage, and transportation is the network.
The coverage details how Kalanick has spent eight years in "near-total silence," building a hidden infrastructure that now includes food delivery robotics and autonomous mining. McCormick points to Lab37's Bowl Builder, a machine that can assemble 300 bowls an hour, as proof of concept. "It is yet to be seen whether he can turn all of those pieces into one really big computer, if he ends up with a disconnected menagerie of good businesses, or if the whole thing crumbles under its own weight." This uncertainty is the crux of the argument: Kalanick is betting that the complexity of physical systems can be abstracted away, much like cloud computing did for servers.
McCormick draws a subtle parallel to the history of land art, suggesting that just as artists once transformed vast landscapes into singular statements, Kalanick is attempting to transform the entire physical economy into a single, programmable entity. The author's tone is one of cautious excitement, acknowledging the "chip on any billionaire's shoulder" while recognizing the potential for trillions in value creation. Critics might note that this "computer model" of physical industries often underestimates the friction of labor, regulation, and local supply chains that software can easily bypass. Yet, the sheer momentum of the investment suggests the market is willing to bet on the abstraction.
"Travis Kalanick waging war on some of civilizations' biggest challenges, all at once, with potentially the biggest chip on any billionaire's shoulder and a desire to make Benchmark miss out on trillions of dollars in returns."
The AI Disruption of Mathematics
The second section shifts from physical infrastructure to the abstract realm of algebraic geometry, where McCormick reports on a stunning development: the disproof of the Jacobian Conjecture by an AI-assisted mathematician. The piece describes how Levent Alpöge, working with Anthropic's Claude, demonstrated that a specific polynomial map is not globally reversible, shattering a decades-old assumption. McCormick captures the surreal nature of this event, noting that the AI was used not to prove a theorem, but to find a counterexample.
"The bigger point is that these models, pointed by human mathematicians, are going on a conjecture disproving SPREE," McCormick writes, highlighting the speed and efficiency of the new toolset. The author contrasts this with the traditional pace of mathematical discovery, where a single proof can take years of solitary work. The coverage includes a reaction from philosopher Joscha Bach, who jokingly proposed a moratorium on AI disproving conjectures, arguing that "it's so much easier to destroy than to build!"
McCormick's analysis suggests that this is the "4-minute mile of disproving longstanding conjectures," a threshold that, once crossed, changes the landscape of what is possible. The author implies that the barrier to entry for high-level mathematical research has effectively collapsed, allowing for a rapid-fire testing of hypotheses that was previously impossible. This aligns with the broader theme of the newsletter: the acceleration of progress through new tools. However, a counterargument worth considering is that while AI can efficiently disprove, the creative leap required to construct new, robust theories may still be uniquely human. The risk, as McCormick hints, is a flood of negative results that may not translate into constructive knowledge.
A New Golden Age for Science Funding
The final section of the commentary turns to policy, examining a 123-page report from the White House science advisor, Michael Kratsios, titled "Science: A New Golden Age." McCormick frames this as a direct challenge to the status quo of federal research funding, which has remained largely unchanged since Vannevar Bush's 1945 report. The author argues that the current system, while good at filtering out nonsense, is "less good at distinguishing potentially revolutionary science from merely solid science."
McCormick details the report's proposal to treat the $200 billion annual R&D budget more like a venture capital portfolio. This includes "fast grants" for exploratory projects, "golden tickets" for unconventional ideas, and long-horizon funding for top talent. "Researchers spend nearly half of their time on administrative work," McCormick notes, emphasizing the inefficiency of the current model. The report suggests that if the government adopted the Howard Hughes Medical Institute's model of minimal reporting and high tolerance for failure, scientists could produce high-impact work at nearly twice the rate.
The author connects this to the broader "Progress Studies" movement, citing figures like Patrick Collison and Sam Rodriques. McCormick's framing is optimistic, suggesting that the executive branch is finally recognizing that the old ways of funding science are calcified and insufficient for the challenges of the 21st century. "It calls for more DARPA-like program managers who can construct a thesis, recruit teams, and make a coordinated portfolio of bets." This is a powerful argument for structural reform, moving away from consensus-based funding toward a more aggressive, thesis-driven approach.
Critics might argue that shifting to a portfolio model introduces too much risk and could lead to the funding of fringe or pseudoscientific ideas. The report acknowledges this by proposing "metascience units" to test these new mechanisms, but the political will to implement such radical changes remains uncertain. McCormick, however, sees this as a necessary evolution, a "greatest-hits album" of the best ideas in science policy finally being assembled into a coherent strategy.
Bottom Line
Packy McCormick's commentary succeeds by connecting disparate threads of technology, mathematics, and policy into a single narrative of acceleration and disruption. The strongest part of the argument is the identification of a shared theme: the need to break down old structures, whether they are corporate silos, mathematical conjectures, or bureaucratic funding models. The biggest vulnerability lies in the assumption that these new tools and strategies will scale without significant friction or unintended consequences. Readers should watch for how the White House's new funding proposals are implemented in the coming budget cycle, and whether Kalanick's physical computing vision can survive the transition from stealth mode to public scrutiny.