The Jagged Frontier: Reading the 2026 Stanford AI Index

Every year, Stanford's Human-Centered AI Institute releases its AI Index — a careful, voluminous attempt to map where artificial intelligence actually stands. The 2026 edition, just released, runs over 400 pages. On the newest episode of Modem Futura, Sean Leahy and Andrew Maynard work their way through its top takeaways and sit with what the data is — and isn't — telling us.

The report opens on a striking juxtaposition. Today's frontier AI models can win gold medals at the International Mathematical Olympiad, yet still stumble on tasks as ordinary as reading an analog clock. Stanford's researchers call this the jagged frontier of AI — and it's more than a quirk. It's a reminder that these systems are not human intelligences being perfected. They are something structurally different, with capabilities and failure modes that don't map neatly onto ours. The interesting question isn't how close AI gets to human thinking. It's what becomes possible when we stop asking it to.

A second thread running through the 2026 Index is the lag in responsible AI. Safety benchmarks are falling behind capability. Incidents are rising. And, as Maynard points out in the episode, the conversation keeps collapsing “responsible” AI into “ethical” AI — two related but meaningfully different things. Ethics gives us the framing. Responsibility asks us to make real, pragmatic, often messy decisions about value, trade-offs, and whose futures we're building toward.

The education findings are equally hard to look away from. Over 80% of students are now using AI for school-related tasks, yet only half of middle and high schools have AI policies in place — and just 6% of teachers describe those policies as clear. Learning is happening. Institutional support is not yet meeting it.

Other findings threaded through the conversation: the closing US–China model performance gap, the fragile TSMC chokepoint at the center of global AI supply chains, and the fifty-point perception gap between AI experts and the public. Each opens a different kind of question about how this technology is being built, distributed, and absorbed.

None of these tensions resolve cleanly — and that's part of what makes the Index valuable. It gives us a shared map for a landscape that keeps shifting under our feet.

📘 Read the 2026 AI Index: https://hai.stanford.edu

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Asimov's "The Fun They Had" and the Real Cost of AI-Driven Education

The History of our Future

More than seventy years ago, Isaac Asimov imagined a future where children learn in isolation, guided by personalized mechanical tutors, and books are relics of a forgotten age. His 1951 short story, "The Fun They Had," is set in 2155, but its questions feel startlingly current.

In the story, a young girl named Margie discovers a paper book and learns about a time when children went to school together—sat in classrooms, were taught by human teachers, and shared the experience of learning with their peers. Her own education is efficient, personalized, and lonely. Her mechanical teacher can diagnose her struggles and recalibrate its approach, but it cannot inspire her, connect with her, or make her feel like she belongs to something larger than a lesson plan.

“Asimov didn’t predict AI as we know it. But he predicted the question that matters most: in our rush to optimize education, are we designing out the very things that make learning meaningful?
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This is precisely the tension at the heart of today's conversation about AI in education. The promise of AI-powered tutors is real and, in many cases, genuinely valuable: adaptive pacing, instant feedback, content tailored to individual needs. But when personalization becomes the dominant paradigm—when every learner is on a separate track, in a separate space, at a separate time—the communal dimensions of education begin to disappear.

Natural Human Impulses for Learning (not schooling)

John Dewey argued more than a century ago that learning is driven by four natural impulses: inquiry, communication, construction, and expression. Most of these are inherently social. They depend on friction, dialogue, surprise, and the presence of other people. No amount of algorithmic sophistication can fully replicate the moment a teacher's unexpected enthusiasm shifts a student's entire trajectory, or the experience of working through difficulty alongside peers who share the same struggle.

Asimov's story also raises a subtler question about what endures. The book Margie discovers has survived two centuries. The static words on the page—unchanging, tactile, physical—carry a kind of permanence that digital media cannot easily match. This resonates with the growing cultural appetite for analog experiences: vinyl records, film photography, even old iPods. These are not acts of technological rejection. They are expressions of a deeper need for embodied engagement, deliberate choice, and the kind of friction that gives experience its texture.

Where do we go next?

None of this means AI has no place in education. It does, and increasingly will. But Asimov's story is a quiet reminder that the most important things about learning—curiosity, connection, belonging, the joy of shared discovery—are not problems to be optimized. They are human experiences to be protected.

The question is not whether AI can teach us. It's whether, in building systems that teach us more efficiently, we are designing out the very things that made learning worth having in the first place.

*Episode 71 of Modem Futura explores these themes through Asimov's story and a wider conversation about technology, nostalgia, and what it means to learn as a human being.*

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Subscribe to Modem Futura wherever you get your podcasts and connect with us on LinkedIn. Drop a comment, pose a question, or challenge an idea—because the future isn’t something we watch happen, it’s something we build together. The medium may still be the massage, but we all have a hand in shaping how it touches tomorrow.

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Futures of Agentic AI and the 2025 AI Action Plan – Episode 42

A Wet Hot AI Summer: Decoding the U.S. AI Action Plan & the Agentic‑Bot Boom

If you stepped away from the screen / feed for even a moment this July, you might have missed two massive AI stories that could shape the near-term innovation in AI. First, the White House released its 2025 AI Action Plan—a 20 plus page blueprint built on three pillars: (1) Accelerate AI innovation, (2) Build national AI infrastructure, and (3) Lead global AI diplomacy. If that wasn’t news enough - just back on July 17th OpenAI, announced the roll out of its new “Agent” modes—autonomous-ish bots that promise to book your travel, manage your calendar, and even spend your money while you sleep. Joking aside - please be VERY careful about what sort of access, privacy, and information you give any automated service. Ask yourself “what would be the worst that could happen?” If the answer makes you cringe or sweat - don’t do that thing. Okay - PSA cautionary rant over… back to the episode notes.

In our latest Modem Futura episode, Andrew and I pull these threads together. We ask whether the Action Plan’s “build‑baby‑build” mantra—complete with massive semiconductor subsidies and calls to “remove regulatory barriers”—is a bold vision or reckless speed run. We also spotlight what’s missing: robust guard‑rails for deepfakes, algorithmic bias, and the colossal energy footprint of new data‑centers.

Switching to agentic AI, we run real‑time tests on OpenAI’s new Agent Mode and compare them with Manus’ more mature workflow. Yes, watching a bot open browser tabs for you is technically impressive—until you realize you can still do most tasks faster yourself . That friction sparks a wider debate:

Productivity paradox – Studies already show teachers and coders spending more time fact‑checking AI output than drafting from scratch.

Privacy trade‑offs – Granting an agent access to your email or bank account may save clicks now, but what’s the long‑term cost to autonomy?

Deepfake backlash – The Plan flags courtroom deepfakes as a national‑security risk, yet leaves broader social harms largely unaddressed.

Behind the policy prose and flashy demos lurks a wider narrative of tech nationalism. The document casts AI as a race the United States must win, positioning allies as followers and China as the ultimate adversary. That framing risks turning open research into a geopolitical arms sprint—one where ethical reflection gets lapped by hype.

So where does that leave forward‑thinking professionals, educators, and creators? We advocate to start the conversations now - here are some great starting topics to begin with:

Stay curious but critical. Piloting new agent tools is the best way to spot real value—and red flags—early.

Advocate for “responsible speed.” Innovation and regulation are not mutually exclusive; demand both from vendors and policymakers.

Own your data literacy. Whether you’re vetting deepfake evidence or AI‑generated lesson plans, will skepticism become a core career skill?

🎧 Tune in for the full discussion—including Hitchhiker’s Guide jokes, live agent fails, and pragmatic optimism about building a flourishing, not merely faster, future.

🎧 Listen on Apple Podcasts: https://apple.co/4l7eCKC

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If you’d like to dive deeper, jump into the link and listen to the podcast or watch the YouTube video. Join us as we explore the forces shaping our collective future and the urgent need to keep human values at the heart of innovation.

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