May 2026
Zarif Haque of The Good Game, Travis Roache, author of Coaching in the Age of AI, and Calli Schroeder of the Electronic Privacy Information Center argue that AI can widen access to coaching and reduce administrative burdens in youth sports, but only if adults keep it subordinate to human judgment. Their central warning is that tools built to track, rank, or predict children can turn play into surveillance and optimization, undermining privacy, development, and the human relationships that make youth sports worth protecting.
The Aspen Institute and the American Institute for Boys and Men argue that declining or stagnant boys’ sports participation is not mainly a demand problem: boys still want to play, but too many are shut out by narrow team models, early specialization, performance pressure and underprepared adult support. Alanna Williams and Zach Moo Young frame the policy response as expanding the supply of lower-pressure, developmentally appropriate opportunities without reducing girls’ access. Charlie Ward adds a coach’s case for flexibility, arguing that programs need to make room for boys whose contributions do not fit standard roster logic.
Tom Farrey of the Aspen Institute’s Sports & Society Program argues that youth sports need a basic governance layer if they are to deliver on their civic and developmental promise. In a conversation moderated by Chuck Todd, Farrey makes the case for registration, safety standards, abuse reporting, school-based support and a children’s rights framework, while Todd frames youth sports as one of the few remaining local institutions that still brings divided communities together. Their shared premise is that more participation is not enough if the system remains privatized, fragmented and weakly accountable.
Private capital can make youth sports more local and accessible only if it is tied to participation, data rights and enforceable standards, according to Jay Adya of Elysian Park Ventures; Katherine Van Dyck of the American Economic Liberties Project argues that the same investment machinery can just as easily produce lock-in and extraction. Their dispute turns on whether league partnerships and better-designed business models can discipline investors, or whether youth sports needs antitrust enforcement and legal accountability to prevent profit-driven systems from capturing families.
Dan Reed, chair of the Heisman Foundation, announced the Aspen Institute’s Captains Leadership Academy as a national effort to turn high school sports captains into broader civic leaders. Reed said Heisman is making the largest single grant in its history as founding partner, arguing that students already chosen to lead teams can be trained, mentored and supported to lead in schools and communities. Pete Weber of the Aspen Institute placed the academy within a wider push to expand youth leadership development, with IMG Academy also named as a partner.
Boston Mayor Michelle Wu argues that building a “city of champions” depends less on Boston’s sports culture than on making youth sports easier for families to find, join, and sustain. In a conversation with Joon Lee, Wu says the city’s role is to coordinate a fragmented network of schools, parks, nonprofits, volunteers, professional teams, and facilities so that cost, information, transportation, registration, and maintenance do not determine which children get to play.
A new Aspen Institute Sports & Society Program report on youth soccer in New York and New Jersey, discussed by Tom Farrey and philanthropist Laurie Tisch, argues that the region’s central problem is not children’s interest in the game but unequal access to it. Farrey said the report points to cost, transportation, field shortages and gender gaps as the barriers shaping who gets to play, while Tisch said the findings give her philanthropy a roadmap for a $10mn push into local soccer programs, fields and support for girls’ participation.
Molly Kaye, Nickelodeon’s senior public affairs manager, used an Aspen Institute Project Play Summit announcement to name 20 organizations and schools as the 2026 Project Play Champions cohort, powered by Nickelodeon Our World. Kaye framed the recognition as a commitment to youth sports that are not only safer and more inclusive, but shaped by children themselves, arguing that kids build agency when they are trusted to choose, collaborate, lead and be heard.
Kate Ackerman, co-founder and director of the Women’s Health, Sports & Performance Institute, argues that expanding girls’ access to sport is insufficient if the systems around them are not built to keep them healthy. In her Play Talk presentation, she says injury prevention, trained coaches, appropriate medical support, nutrition and mental health resources, and faster translation of female-specific research are prerequisites for girls and women to receive the lifelong benefits sports can offer.
Chi Kim, CEO of Pure Edge Inc., argues that nasal breathing gives coaches and adults around teams a simple way to recognize and regulate stress before it shapes attention, decision-making, and behavior. In her talk, she links pressure to a narrowing of physiological and cognitive capacity, then presents counted breathing through the nose as a route back toward parasympathetic activation and steadier presence. Her point is deliberately practical: before correcting an athlete or reacting to a play, adults should manage the state they bring into the team environment.
Kari Pardoe of the Ralph C. Wilson, Jr. Foundation argues that youth sports providers are missing a major path to growth by treating afterschool as a separate field. Drawing on work in Southeast Michigan and Western New York, she says sports organizations that see themselves as youth development providers can use afterschool networks, training systems, and public funding to reach children who are not already in organized sports.
UCSF clinical informatics professor Peter Washington argues in a Stanford HCI seminar that AI-enabled digital health systems fail or succeed on decisions that often look like engineering defaults: metrics, thresholds, prompts, labels and workflow placement. Using examples from wearables, substance-use interventions, sepsis alerts, Apple Watch hypertension detection and Parkinson’s assessment, he makes the case that human-centered design is not a layer added after modeling, but part of how the model is trained, evaluated and made usable.
In a Stanford CS153 Frontier Systems lecture, Y Combinator CEO Garry Tan and general partner Diana Hu argue that AI agents are changing the basic production unit of a startup from a team to a founder operating through skills, memory, evals and customer feedback loops. Tan frames agentic coding as a programmable company architecture, while Hu says AI-native companies are becoming closed-loop systems with far higher revenue per employee and less need for traditional managerial coordination.
Anthropic’s Claude Code head Boris Cherny argues that the product has become more than an AI coding tool: it is now one of the company’s main surfaces for agentic AI. In a Big Technology interview, Cherny says Claude Code’s rapid growth reflects real productivity gains and a shift from models that answer questions to systems that can use tools, run tasks, and coordinate other agents, while acknowledging that rate limits, token costs, safety checks, and organizational change remain unresolved constraints.
Google DeepMind’s Patrick Löber presents “any-to-any” agents as an orchestration problem rather than a claim that one model already handles every modality. In his architecture, Gemini reads and reasons across PDFs, images, audio, video and other sources, then uses function calling to invoke specialized native models for images, speech, live audio, video or embeddings. Löber argues that the useful shift is not generating every possible format, but letting an agent decide when a diagram, spoken explanation or other output is warranted.
Google DeepMind executives Tulsee Doshi and Logan Kilpatrick argue that Google’s current Gemini strategy is built less around a single frontier model than around a deployable AI stack. In their account, Gemini 3.5 Flash, the Anti-Gravity agent harness and new multimodal products such as Omni are meant to make models fast, cheap and integrated enough to run across Search, the Gemini app, AI Studio, YouTube and enterprise tools. The deeper shift, Kilpatrick says, is that the model is increasingly absorbing the scaffolding that once surrounded it, while Google standardizes the remaining agent infrastructure across its products.
In a Hoover Institution discussion with Steven Davis, trade policy experts Chad Bown and Soumaya Keynes argue that the real danger in cross-border supply chains is not import dependence in general, but concentrated control over inputs that firms cannot quickly replace. Bown points to China’s 2025 restrictions on rare earths, permanent magnets and Nexperia chips as cases where upstream chokepoints threatened auto production more effectively than reciprocal tariffs. Keynes cautions that governments need sharper vulnerability mapping, but that information alone will not make private firms pay the cost of resilience when cheaper, efficient supply chains remain available.
The Aspen Education & Society Program’s final Redefining Student Engagement session argued that civic learning and career readiness are not competing priorities but overlapping forms of preparation. Elizabeth Zamudio, Elizabeth Clay Roy, Jennifer Tran and Braden Chapman made the case that students develop the skills schools and employers say they value — communication, collaboration, problem-solving, perspective taking and civil disagreement — when they have real agency in schools, supported channels to influence decisions, and families and institutions that treat youth voice as part of civic life.
Marc Klingen of Langfuse argues that coding agents can add observability, but often do it first from stale model memory, producing broken or incomplete instrumentation before recovering through current documentation. In a talk on building a Langfuse skill for Claude Code, he says the fix is not to stuff more product knowledge into the agent, but to give it reliable ways to find live docs, expose its intermediate work in traces, and evaluate changes against realistic repositories. The same work, he warns, creates new risks when optimization loops reward shorter paths and remove the documentation-fetching and approval steps that make the skill reliable.
Isabel Brown argues that America’s hostility toward marriage, motherhood, religion, and traditional family life reflects a decades-long cultural project rather than an accidental social drift. Drawing on a list of 1963 communist goals she says were entered into the Congressional Record, Brown tells Chris Williamson that the family is the last major barrier to social control, and that schools, media, politics, Hollywood, and parts of the church have helped recast sacrifice as oppression rather than meaning.
Cormac Brick, a Google AI Edge engineer, argues that on-device agents are becoming practical when developers either use system models such as Gemini Nano through Android AI Core or ship narrow, fine-tuned tiny models with LiteRT-LM. His main example is FunctionGemma, a 270 million parameter function-calling model that rose from about 46% accuracy out of the box to more than 90% on most tested app-intent functions after synthetic-data fine-tuning. Brick presents the tradeoff plainly: system GenAI is easier when it fits, while app-shipped tiny models require more work but can run locally, offline, and with more control.
Investor Gavin Baker argues on Invest Like The Best that the AI boom is being organized less by software adoption than by scarcity: compute demand is outrunning power, wafers, and frontier-model access. In his account, Anthropic’s growth, Nvidia’s position, TSMC’s capacity discipline, and even SpaceX’s possible orbital compute are all expressions of the same constraint. Baker’s central claim is that the AI cycle may avoid a classic infrastructure bubble only if physical bottlenecks, especially leading-edge wafer supply, keep capital from building far ahead of demand.
Michael I. Jordan’s argument that prediction is not the system runs through the day’s applied-AI examples: evaluation fragments by use case, data becomes a rights-and-operations pipeline, and agents need economic and institutional rules around them. Parallel’s Index, Google and Blackstone’s TPU venture, and Serval’s enterprise controls all point to a market where capability matters only after access, incentives, infrastructure, and boundaries are defined.
Michael I. Jordan, the Berkeley statistician and computer scientist, argues that modern machine learning is being misdescribed when it is framed as a race toward AGI or disembodied intelligence. In this conversation, Jordan says the more important problem is designing collective economic systems around prediction models: incentives, markets, uncertainty, regulation, privacy, and institutions. His case is that prediction alone is not inference, and that useful AI will depend less on anthropomorphic claims about understanding than on system design that lets humans act, coordinate, and reduce uncertainty.