Noah Smith delivers a rare, data-driven reality check on the state of American society, cutting through the noise of political theater to reveal a world where crime is falling, AI is solving century-old math problems, and the economic myths underpinning mass deportation are crumbling. This roundup doesn't just report the news; it challenges the foundational assumptions of the current political moment, forcing a confrontation between what voters believe and what the data actually shows.
The Crime Paradox and the Path Forward
Smith begins by addressing the cyclical nature of political outrage over public safety. He notes that while falling crime rates are often celebrated, the silence during rising crime years suggests a selective memory. "People who use crime drops to wave away the need for further intensified policing... completely ignore the very high baseline level of American violence," Smith writes. This is a crucial distinction: progress is real, but the baseline remains dangerously high compared to peer nations.
The data he presents is striking. Citing Axios, he notes that "homicides dropped 17.7%" and "robberies fell 20.4%" across major U.S. cities in early 2026. Smith attributes this to a combination of local enforcement and a reduction in popular unrest, though he wisely cautions that definitive evidence is still emerging. He argues that these drops should be viewed as pilot programs for systemic change rather than a reason to declare victory. "Real progress is possible," he asserts, but only if we acknowledge that the job is far from finished.
Critics might argue that focusing on aggregate numbers obscures the uneven distribution of safety across different neighborhoods. However, Smith's point stands: the narrative that America is incorrigibly violent is empirically false, even if the work to close the gap with Europe remains.
"Successful crime reductions in particular cities can serve as pilot programs, giving us ideas about how to fight crime more systematically across the country."
The Economic Fallacy of Mass Deportation
The piece takes a hard turn toward the economic consequences of the administration's recent immigration enforcement. Smith dismantles the long-held belief that removing immigrant labor will automatically boost wages for native-born workers. "Immigration — even low-skilled immigration — creates a labor demand shock that balances out the labor supply shock," he explains. The logic is simple: immigrants are not just workers; they are also consumers who create demand for goods and services.
When the executive branch began a massive wave of arrests and deportations, the expected economic windfall for the working class failed to materialize. Smith highlights a new paper by Cox and East, which found "no effect, and possibly even a small negative effect" on native-born workers in affected industries. The disruption to supply chains and local economies was so severe that it reduced demand for everyone, not just immigrants. "If you hurt an industry, you hurt everyone in that industry," Smith concludes, pointing to the concept of increasing returns to scale.
This analysis suggests that the true motivation behind anti-immigration rhetoric is not economic protectionism, but something else entirely. "The more these null results come in, the more the true concerns of the anti-immigration people become clear," Smith writes, hinting at cultural and political anxieties rather than labor market dynamics. The long-term cost may be permanent inefficiency, leaving consumers with "more expensive fruit from now on."
The Global Anxiety Over Artificial Intelligence
Perhaps the most unsettling section of the roundup concerns the rapid shift in public sentiment toward artificial intelligence. Despite the U.S. leading in AI development, Americans are increasingly hostile toward the technology. Smith cites a commencement address where Eric Schmidt was met with a "chorus of boos" for discussing the "technological transformation" of AI. The speed of this backlash is unprecedented, with polls showing AI anxiety rising faster than any other political issue.
Interestingly, this fear is not unique to the West. Smith points to research by Matt Sheehan showing that even in China, where public sentiment toward AI is typically positive, concerns about job displacement have skyrocketed. "In 2024, the Chinese participants ranked AI's impact on jobs second to last... In 2026, they ranked it second from the top," Smith notes. This global convergence of fear suggests that the economic disruption of AI is a structural reality, not a cultural misunderstanding.
The irony is palpable. "AI industry leaders' habit of going in public and constantly saying that their technology's purpose is to put everyone on the welfare rolls for all eternity had exactly the kind of result you'd expect," Smith observes. The industry's own messaging may have accelerated the backlash it now fears.
"AI is not yet as unpopular as Donald Trump, the Democrats, the GOP, ICE, or Iran, but it's getting up there."
The Myth of the Oligarchy and the Simpson's Paradox
Smith challenges a pervasive narrative among progressives: that the United States has always been an oligarchy. He traces this belief to a widely cited but misinterpreted 2014 paper by Gilens and Page. Smith argues that the paper's conclusion—that policy outcomes align with the preferences of the wealthy—was a statistical artifact. By focusing only on cases where the rich and poor disagreed, the authors fell prey to Simpson's Paradox, a statistical phenomenon where a trend appears in different groups of data but disappears when these groups are combined.
"Peter Enns has a cool new paper explaining why Gilens and Page's famous paper doesn't warrant the conclusions that everyone tends to draw," Smith writes. When the full dataset is considered, the responsiveness of policy to the wealthy disappears, suggesting that before the current administration, the middle class still had a voice. "We lost something important when Trump was reelected," Smith concludes, framing the current era not as a continuation of oligarchy, but as a sharp, unprecedented deviation from the norm.
The AI Breakthrough That Changes Everything
The roundup ends on a note of awe, detailing how AI has solved a major open problem in mathematics that had stumped humans for nearly 80 years. The "planar unit distance problem," posed by Paul Erdős in 1946, was recently cracked by an internal OpenAI model. The solution didn't just confirm existing theories; it disproved a longstanding conjecture by using insights from algebraic number theory.
This achievement signals a shift in the nature of intelligence. "Top professional mathematicians are now saying that the job of 'mathematician', as we know it, may be very rare very soon," Smith writes. The implication is profound: high-IQ cognitive tasks, once thought to be the last bastion of human uniqueness, may be the first to be automated. Smith argues that superintelligence doesn't require superhuman reasoning, but rather "human-level reasoning, combined with encyclopedic knowledge, computer-like speed, and a very large working memory."
This capability allows AI to bypass the "burden of knowledge" that has slowed human innovation. The result is a future where the pace of discovery accelerates beyond human comprehension, driven by machines that can connect disparate fields of study in ways humans cannot.
"Superintelligence comes from the computer-like parts of AI, not the human-like parts; the human-like parts were simply the last necessary piece of the whole package."
Bottom Line
Smith's commentary is a masterclass in separating signal from noise, using hard data to dismantle popular myths about crime, immigration, and political power. Its greatest strength is the willingness to challenge the assumptions of both the left and the right, while its biggest vulnerability lies in the uncertainty of long-term economic predictions in a rapidly shifting landscape. The reader should watch closely as the AI revolution moves from solving math problems to reshaping the labor market, a transition that will likely redefine the social contract in ways we are only beginning to understand.