Requests for Curiosity Summer 2026
If you're wrestling with these questions, we want to meet you
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.
That doesn’t mean going alone.
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.
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.
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.
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
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.
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
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.
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
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.
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
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.
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
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.
Are world models necessary for widespread deployment of general robotics, and if so, what remains unsolved to make them work?
What still needs solving in hardware and the supply chains even when the software layer works?
What kinds of infrastructure changes (in software, hardware, and the built environment) will be needed to safely deploy more capable robots?
Reach out to Jonathan
What is the future of proprietary intelligence?
Nearly every consumer app is built on roughly the same intelligence, available to everyone at falling prices. But the data that feeds that intelligence will remain proprietary, and problems surround temporal memory: fast decay, inaccuracy, and volatile costs.
What will be the right architecture for long-horizon user memory?
If users own their personal memories, how will they be stored, ported and retrieved?
What does this mean for privacy and regulations—how will data-portability rules, deletion rights, and user-owned-memory standards evolve?
Reach out to Prateek
What does an AI-driven shift toward consumer-controlled medicine look like?
For decades, tech-forward patients and advocates have tried to wrest control of their health choices and data from the medical establishment. AI may radically expand the ease of accessing medical data and its value, while collapsing the cost of reasoning.
Will we see new AI-driven biomarkers used to continuously manage biological systems?
When AI medical advice is good enough and nearly free, do consumers start bypassing doctors entirely for routine health decisions?
Can we build data consent infrastructure as simple as open-source licensing that makes mass participation frictionless?
Reach out to Mark
Are smart devices finally smart enough?
Smart devices promised to rewire how we live, and instead were clunky interfaces with marginal utility. With models small enough to run on-device, hardware can perceive, reason, and act locally.
Who owns the platform layer when everything is ambient?
If on-device AI removes the cloud dependency, does that unlock new monetization or does it just make a commoditized hardware product slightly better?
Does the first mover define the protocol and data layer for everything that follows, or does each category fragment into its own silo?
Reach out to Dylan
In a world where work becomes optional, where does meaning come from?
Work can provide structure, status, belonging, and motivation. Universal basic income can replace a paycheck and none of the rest. The hard problem isn’t money, but meaning. Assuming the struggle that accompanies a universal basic income, there will be a market for the provisioning of meaning itself.
What provisions break when work, as we know it today, goes away?
Can you create artificial arenas to provide meaning, like many simulation games already do?
What is a secular, buildable version of religion?
Reach out to Rohan
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