Requests for Curiosity Summer 2026 - South Park Commons

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Requests for Curiosity Summer 2026<br>If you're wrestling with these questions, we want to meet you

South Park Commons<br>Jun 17, 2026

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Great ideas aren’t found in charted territory. Reaching them means wandering off the well-worn path and staying lost long enough to find a new frontier.<br>That doesn’t mean going alone.<br>Curiosity compounds, and it compounds faster in good company. The people you explore alongside widen your surface area for discovery. Their problems send you somewhere you wouldn’t have looked. The more you ask, the more you find.<br>South Park Commons exists to push the frontier by gathering the right people. The questions we explore at SPC are indicators of where the frontier ends up next.<br>If you’re thinking about any of these questions, reach out to the SPC team member below, or apply to join SPC directly.

Can AI express more than just intelligence?

AI is on the trajectory to be the world’s most intelligent lawyer, doctor, and accountant. But empathy, curiosity, connection and humanity are integral to those professions.<br>Is it possible for AI tools to truly develop empathy in a way that would be deeply felt by humans interacting with them?

How can AIs foster a deep connection with humans?

What is the form factor that AIs should have in order to enable connectivity?

Reach out to Aditya<br>What markets emerge when supply is no longer constrained?

Many successful companies emerged by unlocking previously inaccessible supply through making it discoverable, accessible, and economically viable. Expertise can now be replicated, services can be partially automated, and a single person can serve dramatically more customers than before. Markets that were previously constrained by scarce human expertise may suddenly become viable.<br>What marketplaces were historically impossible because supply was too scarce?

Which marketplace ideas failed historically because they arrived before AI made the economics work?

Which marketplaces become more defensible as AI makes software easier to build?

Reach out to Danh<br>What needs to get built for a world that stays abundant as AI turns energy into a scarce, contested input?

In 2005, Thomas Friedman called it a flat world: cheap energy, free trade, stable geopolitics, abundant labor. Recently, our world has started to look more jagged than flat. One example: AI training and inference are turning electricity into a contested input just as stationary battery costs fall along a Wright’s Law trajectory, pushing home storage toward a default appliance.<br>What new tools, regulatory changes, or business models are needed to get breakthroughs to market faster?

What are the components, materials, models, and software that underpin critical infrastructure?

If enough homes localize storage, does the grid’s fixed cost collapse onto fewer customers until the system unravels, and who profits from re-bundling the defectors?

Reach out to Evan<br>What does it mean to learn and how should AI reshape pedagogy?

New education models promise a hyper-personalized and accelerated pace of learning. As AI makes information more accessible, instilling depth of understanding isn’t guaranteed.<br>How much of comprehension depends on effort, on the friction of figuring something out yourself?

Is memorization an outdated proxy for understanding, or is it a foundation we’re too quick to dismiss?

How do you build AI tools that guide and inspire curiosity without short-circuiting the process?

Reach out to Arian<br>What kind of organization gets built when companies run their own fleet of models?

The tools, workflows, management structures, and capital models we spent years building around human knowledge workers are becoming obsolete, and the operating models that sit on top of them need to be rebuilt. The assumption is that enterprises will rent intelligence from a handful of frontier labs, but that’s fragile. Open-source models are less than a year behind, fine-tuning keeps getting cheaper, and companies are learning to capture their own organizational context. The infrastructure for this future barely exists yet.<br>How do companies systematically capture organizational context and turn it into training data?

Where do decision rights sit between humans and agents, how do cross-functional teams form when the units are specialized agents, and who owns the seams between them?

What happens to selling, procurement, and partnership when the counterparties on the other side of the table are also agents operating at machine speed?

Reach out to Finn<br>Where are the robots?

Despite a huge influx of capital, we’re nowhere near widespread deployment of robotics in complex, generalized environments. ‘World model’ has become a punchline in funding circles for its imprecision and overuse. It seems plausible we’ll get recursively self-improving AI before we get a robot that can load a dishwasher.<br>Are world models necessary for widespread...