In 1931, amid the hardships of the Great Depression, historian James Truslow Adams gave enduring expression to an idea that had circulated in American life for generations. The American Dream, he wrote, was “that dream of a land in which life should be better and richer and fuller for everyone, with opportunity for each according to ability or achievement” (1). Adams deliberately distinguished the Dream from the mere accumulation of wealth. He envisioned a society in which individuals, regardless of birth, could develop their talents, pursue meaningful lives, and participate in a community of expanding opportunity.
The seven pieces below were chosen against a deliberately narrow test — not patriotism, not heroism, not sacrifice, but the quieter question of what makes Americans humane. Each holds up to reflection rather than applause. Several were picked specifically because they are not the pieces you’d expect on a Fourth of July list — one indicts the holiday outright, one is a mother’s refusal to raise sons for anyone’s war, one is a victorious wartime president who will not gloat. Read together, the seven span 1782 to 1960, are arranged chronologically, and argue with each other as much as they agree.
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Two and a half centuries after fifty-six delegates to the Second Continental Congress affixed their signatures to a document announcing a new kind of nation, it is worth asking what that nation has actually given the world. The record is not without blemish: slavery endured for nearly a century after 1776, racial segregation for another, and the gap between America’s stated ideals and its practiced realities has been the central tension of its history. Yet the ledger, taken whole, shows a civilization that produced constitutional democracy, defeated the worst tyrannies of the modern age, rebuilt a shattered continent, eradicated one of history’s most feared diseases, and fed approximately one billion people who might otherwise have starved. The ten contributions that follow are gifts that, regardless of America’s internal contradictions, have altered the conditions of human life for people who never set foot on American soil.
This report explores what grade school, high school, and university campuses might look like if they were designed from the ground up for a world in which generative and agentic AI systems are commonplace. The exercise assumes that designers are not constrained by inherited school architecture, traditional schedules, or existing administrative practices. Instead, the central design principle is that form follows function. The resulting campuses are intended to support continuous human-AI collaboration, personalized learning, interdisciplinary inquiry, and preparation for a labor market increasingly shaped by intelligent systems.
The BCG and MIT Sloan Management Review’s ninth annual global research study, released in November 2025, surveyed 2,102 executives across 21 industries and 116 countries and found that 35 percent of organizations have already begun using agentic AI — with another 44 percent planning to follow soon (1). Yet the same study identified a widening gap between AI’s pace of adoption and leaders’ readiness to manage it. The World Economic Forum’s 2026 report, Organizational Transformation in the Age of AI, reinforced this finding: only about 15 percent of organizations are fundamentally redesigning work around AI; most are simply automating what existed before (2). And SHRM’s 2026 research underscored a third dimension — that the biggest barrier to AI value capture is not employee reluctance but leadership readiness (3).
David Brooks, in “The People Who Will Thrive in the AI Age” (Atlantic, 28 June 2026), cited two pieces of research that together tell a story both bracing and clarifying (1). The first, from ActivTrak’s Productivity Lab — which analyzed more than 443 million hours of digital workplace activity across some 163,000 workers — found that AI adoption has made work faster, denser, and more demanding, not easier (2,3). Collaboration surged 34 percent, multitasking rose 12 percent, and weekend work climbed more than 40 percent. The second, from UC Berkeley’s Haas School of Business, found that workers who used AI did not use their freed-up time for rest; they used it to take on tasks they had previously outsourced or deferred (4). Xingqi Maggie Ye, a Haas doctoral student, spent eight months observing 200 employees at a technology company and discovered that AI expanded what workers felt capable of and willing to tackle. Scope grew; boundaries between work and personal time dissolved.
Introduction: The following selected quotes from Reed Hastings, Netflix co-founder and former CEO, are from Michael B. Horn & Diane Tavenner’s “Reed Hastings on What It Will Take for AI to be Different from Other Edtech,” The74, 25 June 2026 [https://tinyurl.com/5dhz748u]. For me, Hastings provides the clearest, most grounded foresights on AI and its potential impact on education. -js
Bottom line. AI chatbots are biased, if “bias” means a systematic tendency in outputs rather than a conscious political intention. The best recent evidence points to a left-of-center tilt in many major models on U.S. and European political questions. That does not mean every chatbot always gives liberal answers, or that every finding is equally strong. It means that, across several independent methods, a recurring pattern appears: when models are asked about contested political or social issues, their default framing often looks more liberal, progressive, or center-left than conservative. The finding is strongest for user-perceived slant and for benchmark studies that compare model answers with party platforms or survey instruments. It is weaker when the question is framed as absolute neutrality, because neutrality itself is hard to define in politics.
The earlier ETC Journal “Three Critical Global Decisions” entries treated AI governance as a moving contest among safety, speed, access, and national advantage. The November and December 2025 installments covered the broad regulation-versus-innovation struggle, frontier-model oversight, open versus closed models, and the energy burden of compute. The February 2026 installment focused heavily on the U.S. federal-state clash over AI law, and the April 2026 installment returned to multilateral governance through the United Nations Global Dialogue, asking whether countries would commit to “coherence and interoperability” or allow governance to fragment into blocs (1-4). Those decisions remain alive, but June 2026 has given them sharper geopolitical form. The month’s decisions are less about whether AI should be regulated in the abstract and more about who gets access to the most powerful systems, who controls the hardware and cloud pathways beneath them, and who pays the environmental and grid costs of the race.
Dario Amodei’s October 2024 essay, “Machines of Loving Grace,” is best read not as a checklist of near-term product launches but as a forecast about what could happen after the arrival of what he calls “powerful AI.” His central claim is that AI could increase the rate of biological discovery by at least tenfold, allowing humanity to compress “the next 50-100 years of biological progress in 5-10 years” (1). He explicitly allows for laboratory and clinical latency: animal experiments, hardware design, and clinical trials cannot be reasoned away by software. That caveat matters. It keeps his forecast from being a simple prediction that today’s AI tools, scaled a bit further, will cure infectious disease, cancer, Alzheimer’s disease, and aging by 2030. The better question is whether the world in mid-2026 is showing the kind of acceleration that would make the 2028-2033 window credible.
Something is off in the way data centers are discussed publicly versus how they are understood within the technology industry. Outside observers hear about enormous power consumption, dried-up aquifers, tax breaks handed to some of the world’s richest corporations, and warehouses full of servers that create almost no permanent jobs. Inside the industry, the same buildings look like the physical backbone of a technology transition that will define the next decade — and investors who once worried about a bubble now watch capacity lease up as fast as it can be built.
Recently, the word “loop” is threatening to leapfrog “prompt” as the primary way to work with chatbots. A sentence attributed to NVIDIA CEO Jensen Huang, “Nobody writes prompts anymore. The new job is to write and handle loops,” has been widely repeated in mid-June 2026 discussions of agentic AI, including a Business Insider report on the rise of “loop engineering” and several social-media posts. However, no clean NVIDIA transcript or full video passage has surfaced to independently verify the exact wording. For that reason, this report treats the sentence as a useful public formulation of a real shift, not as a settled archival quotation. The shift itself is well documented: AI use is moving from one-off instructions to systems in which models plan, use tools, inspect results, revise their own work, and continue until a goal or stopping rule is reached. Business Insider describes the trend as a move away from direct prompting toward “self-perpetuating instructions” that let agents work toward completion with less manual intervention (1).
Consumer bionics are beginning to matter to sports, but not in the way science-fiction imagery might suggest. Professional and lower-level teams are not, as a rule, sending players into competition wearing powered exoskeletons. Public evidence points instead to three nearer-term uses: robotic and semi-robotic systems in rehabilitation, AI movement analysis for injury prediction and return-to-play decisions, and a new class of adaptive protective equipment that reacts to dangerous motion while staying light enough for practice or games. The sports version of consumer bionics is therefore likely to arrive first as recovery support and protective gear, not as visible motorized augmentation.
Consumer bionics is no longer a purely medical or industrial story. In 2025 and the first half of 2026, lightweight powered exoskeletons moved into a new public-facing phase: outdoor mobility, hiking assistance, walking support, and fatigue reduction. The clearest examples are Hypershell, WIRobotics, Ascentiz, and Skip with Arc’teryx. These products are not yet household appliances, and they should not be described as widespread in the way smartphones, hearing aids, or fitness trackers are widespread. A more accurate description is early commercial availability: some devices are shipping or sold through direct channels, some are in reservation or pre-order queues, and others are still being demonstrated at CES, in pilots, or in limited institutional deployments.
Introduction: For most of the brief public history of large language models, the word “hallucination” has functioned as a warning label—shorthand for the alarming tendency of AI systems to produce fluent, confident, and entirely fabricated output. Researchers, journalists, and regulatory bodies used it to flag a reliability failure, and rightly so: an AI that invents citations, confabulates statistics, or generates counterfactual histories with the smooth assurance of a well-briefed expert poses genuine dangers to anyone who takes its outputs at face value. The framing made immediate practical sense. But two recent essays in the Educational Technology and Change (ETC) Journal suggest that it may have been directing attention toward the symptom and away from what the symptom portends.
Abstract: This article develops a theory of machine conjecture for an age in which generative and agentic artificial intelligence increasingly augment human thought. It begins with the contemporary problem of AI hallucination: the production of fluent but unsupported claims by large language models. Rather than treating hallucination only as a defect, the article uses it as a historical starting point for examining a larger cognitive problem: how intelligent systems generate possibilities that are not yet justified by evidence. Drawing on philosophy of science, creativity research, critical-thinking scholarship, embodied cognition, agentic AI, embodied science, and emerging AI-literacy work, the article argues that discovery depends on a cycle of generative openness, conjectural preservation, exploration, embodied evaluation, and disciplined closure. Four concepts organize the argument: machine conjecture, conjectural reserve, embodied extrapolation, and conjectural literacy. Machine conjecture refers to the generation, preservation, evaluation, and refinement of possibilities beyond established knowledge. Conjectural reserve names a protected cognitive space in which possibilities can be entertained without premature acceptance or rejection. Embodied extrapolation describes the movement from conjecture to testable interaction with environments. Conjectural literacy names the human capacity to generate, preserve, explore, evaluate, and refine possibilities without prematurely accepting them as true or prematurely rejecting them as false. The article concludes that as generative and agentic AI increasingly augment human thought, the future of discovery may depend less on access to information than on our collective ability to cultivate, evaluate, and navigate possibility spaces.
The term hallucination has become a catch‑all for everything that seems unreliable or unruly about generative AI. It suggests fabrication, error, or a failure of grounding. Yet this framing may obscure something more interesting: the possibility that hallucination is not merely a defect but a form of extrapolation — a generative leap that resembles the speculative, imaginative moves humans make when we think beyond what we already know. This idea has begun to surface in recent scholarship, though often in fragmented or tentative ways. Exploring it requires examining what hallucination actually is, how it emerges from the architecture of large language models, and how embodiment — direct access to the world, whether through physical sensors or high‑fidelity research environments — might transform it from a liability into a cognitive function.
Debates over the future of the U.S. Department of Education (ED) have moved from think-tank white papers to the center of national politics. Under President Donald Trump, the administration has not only proposed deep cuts to the department but has also articulated an explicit “path to elimination,” framed as a way to return control of education to states and families and to reduce federal bureaucracy and waste (1). At the same time, educators, civil rights advocates, and many lawmakers argue that dismantling ED would damage school quality and equity, especially for low-income students, students with disabilities, and students of color (2,3). Because education systems evolve slowly, the consequences of shutting down or hollowing out ED must be evaluated both in the short term (roughly one to five years) and the long term (five to twenty years). Some reforms that appear disruptive or harmful in the near term might yield benefits later, while others could lock in structural disadvantages that are difficult to reverse.
In the spring of 2025, Harvard and Broad Institute biochemist David Liu received science’s largest individual prize for two inventions that have quietly become the foundation of a new kind of medicine: base editing and prime editing. Both technologies let scientists rewrite individual letters of the genetic code, correcting the precise mutation that causes a disease without cutting the DNA strand in two, as older CRISPR tools do. Over the past year, those tools have been joined by a second technology moving at an even faster pace: artificial intelligence. Together, the two are reshaping how genetic medicine gets designed, tested, and delivered to patients.
At the 2026 MIT Sloan CIO Symposium, Abbie Lundberg, editor-in-chief of MIT Sloan Management Review, put one question to a group of technology and business leaders: what have you learned this year about how humans and agentic AI work together (1)? The eleven answers, gathered in a short video released on June 11, 2026, read at first like a grab bag of anecdotes — a worry here, a workflow tweak there, an inspiring vision of augmentation somewhere else. Set side by side, though, they stop looking like eleven separate lessons about a technology and start looking like one long, unresolved argument about something else: what an organization believes a worker is for, and therefore what it believes agentic AI is worth. That argument — not the underlying models — is the real subject of this essay, and it is the reason these eleven minutes of tape matter well beyond the conference room where they were recorded.
Abbie Lundberg: [Introduction: Is your team ready for AI agents?] AI agents are no longer hypothetical. They’re in the workflow. I’m Abbie Lundberg, Editor-in-Chief at the MIT Sloan Management Review, and we are here at the [2026] annual MIT Sloan CIO Symposium. We were asking IT and business leaders, “What have you learned this year about how humans and agentic AI work together?” Let’s hear what they had to say.
A paradox has emerged in the mid-2026 corporate landscape: artificial intelligence is driving an unprecedented surge in specific corporate technical and advisory roles, yet recent university graduates are experiencing the most hostile entry-level market since the turn of the decade (1). This labor polarization ignited the recent headline broadcast by CNN Business, noting that while AI is sparking an absolute jobs boom, it is explicitly “not for newbies” (1,2). The foundation of this disruption lies in an economic restructure termed the “barbell economy” (1). In this paradigm, capital investments are flowing heavily into two distinct extremes: high-end specialized engineering and governance infrastructure on one side, and manual, physical service sectors on the other (1,3). The core casualty is the traditional corporate middle, which has historically absorbed fresh university graduates for foundational training (1).
YouTube has been flooded by generative AI clickbait videos such as “AI Just Revealed the Method Egyptians Used to Cut Granite — And It Doesn’t Make Sense” (1). Its central argument is that an artificial intelligence called Grok analyzed photogrammetric scan data from the granite quarries at Aswan, Egypt, and identified ancient cutting marks so geometrically precise that they could only have been produced by industrial rotary cutting equipment — technology that did not exist until the twentieth century. The implication is that a sophisticated pre-dynastic civilization, or some form of advanced lost technology, shaped Egypt’s hardest stone thousands of years before modern machinery was invented.
The brain-as-muscle metaphor has long served as the moral backbone of academic arguments against labor-saving technology. In this view, trudging through library stacks, compiling reams of notes, and manually searching databases are not simply means to an end—they are the end itself, cognitive reps in a workout that builds intellectual strength. To automate any of this labor, the argument runs, is to skip the gym. The metaphor carries real rhetorical weight, but it rests on a category error: the brain is not a muscle, effortfulness is not equivalent to productive learning, and the removal of mechanical friction does not create a mental void. What it creates is an opportunity. The question for writers and educators is not whether AI has cleared this cognitive space but what to put in it—and how to ensure that what fills it is the kind of thinking that machines genuinely cannot replicate.
Educational Technology and Change Journal 
