Tag: r/problem

Status: Open, 22 May 2026

Problem statement:
What does an exceptional learner understand about learning hard things deeply and quickly that most people miss?

We live in an age of near-infinite educational content. A motivated learner can access lectures, textbooks, papers, simulations, tutors, and LLMs on almost any subject. Yet most learners still fail to convert this abundance into genuine mastery. They collect information, memorize concepts, follow curricula, and complete exercises, but often cannot regenerate the explanations, criticize them, transfer them to new problems, or see why the field is structured as it is.

If the goal of learning is not information acquisition but the creation of internal explanatory knowledge, what is the actual unit of learning, and what process allows a learner to reconstruct a field deeply enough to think inside it? It is not enough to say “study harder,” “learn the fundamentals,” “use spaced repetition,” “follow curiosity,” “solve problems,” or “use AI as a tutor.” Identify the structure that converts external content into internal explanatory power efficiently.

Conjecture: Deep learning is the rapid reconstruction of hard-to-vary explanatory networks inside problem-situations

Status: open, 22 May 2026

Problem statement:
How can an open society preserve unpermissioned knowledge creation when the means of testing, scaling, computing, financing, publishing, surveilling, and enforcing are increasingly concentrated?

Societies stagnate when the bottlenecks required to turn ideas into effects become controlled by institutions that are harder to criticize than the ideas they evaluate.

As AI, compute, finance, medical validation, surveillance, and state coercion become increasingly concentrated, the bottleneck in knowledge creation may shift from discovering true ideas to getting permission to test and deploy them. A person can invent a cancer cure, a better education model, a new AI safety method, or a political criticism, but still lack access to trials, compute, capital, distribution, legal protection, or privacy. If the ability to create knowledge depends on conjecture, criticism, testing, and implementation, what concrete institutions would you build to preserve unpermissioned paths from idea to impact? It is not enough to say “decentralize power,” “regulate monopolies,” “protect freedom,” or “fund innovation.” Identify the specific mechanisms that let individuals and small groups test, validate, criticize, and deploy important ideas without requiring approval from every dominant chokepoint.

Status: solved, 18 May 2026

Problem statement: What stance should one take under difficult circumstances? The answer is neither “Life is happening to me” or “Life is happening for me.”

Tony Robbin’s signature philosophy is “Life is always happening for you, not to you.” It is a powerful frame that has helped many people but brings in some mysticism. What is the more accurate version of it? How could we rebuild it on epistemics that don’t require believing the universe has intentions?

Conjecture: See Life in Ways that Help You

Status: Solved, 17 May 2026

Problem statement:
How can we make the cost of AI generated text legible?

Historically, writing implied that a human being paid attention, struggled, selected, revised, and risked their name on this piece of work. Reading is one of the few ways to spend attention to make contact with another mind, and the implicit deal is that the writer spent something comparable making contact possible. AI breaks that signal.

That does not make their reaction epistemically good. Rejecting an idea because of its origin is a kind of genetic fallacy. But their fear has a rational core: AI makes low-effort, low-accountability text cheap at scale.

My initial answer to this, as I use LLMs freely, was to just mark every post as “Made with AI”. But noticing how upset people were on Twitter made me realize I need to give this more thought. Honest labels were not good enough.

So that makes me ask how can we make the cost of AI generated text legible? Finding a solution to this will not make the people who are offended happy, but hopefully it is an acceptable compromise for now.

Conjecture: AI Writing: Work with the Garage Door Up

Status: Solved, 13 May 2026

Problem statement:
Every person operates within a space of actions they can actually take, which is much smaller than the space of actions physics permits. What determines the size of that space, and what moves it?

Common accounts of agency — as a skill to be built or a trait to be possessed — fail to predict who actually expands their reach in life and who doesn’t. The people we call agentic are doing something these accounts can’t see.

Conjecture: Freewill & The Power to Change Reality

Status: Open, 13 May 2026

Problem statement:
If I led a development-constrained country and wanted to enable the conditions under which my people could create knowledge—criticism being cheap, error being correctable, dissent being survivable, discovery being rewarded—what would I do, and what would I commit to never doing? And how do those conditions take root where they are currently weak?

Dwarkesh posted this question: What should countries which are not currently in the AI production chain (semis, energy, frontier models, robotics) do in order to not get totally sidestepped by transformative AI? If you’re the leader of India or Nigeria, what do you do right now?

But I think this is focusing on the incorrect unit.

Dwarkesh’s question takes the AI production chain as the relevant unit—who makes the chips, trains the frontier models, builds the robots. The relevant unit is a population’s capacity to create knowledge, and that “being in the chain” is downstream of that. If I’m right, a country that solves the deeper problem can be fine without owning fabs or frontier labs, and a country nominally in the chain whose people can’t critically use the outputs is worse off than it looks. That’s a sharper claim than it first appears, and it would survive or fail on specific predictions—e.g., over the next twenty years, do AI productivity gains accrue more to populations with stronger knowledge-creating institutions than to populations that happen to host more compute?

Other reasons for why I am broadening the scope:

727 million people do not have electricity

The number of people without access to electricity worldwide has dropped by more than half between 2000 and 2025, amounting to 727 million in the latter year. The biggest decline was recorded in the developing Asian region, where the population without access to electricity declined by over 90 percent in the period. Meanwhile, this value increased in Sub-Saharan Africa, which accounted for 80 percent of the global total in 2025.

Published by Won So, Mar 16, 2026

2.1 billion people still lack safely managed drinking water

Between 2015 and 2024, 961 million people gained access to safely managed drinking water services, raising global coverage from 68% to 74%. However, 2.1 billion people still lacked safely managed drinking water, 1.5 billion had basic services, 287 million had limited services, 302 million relied on unimproved sources, and 106 million collected water directly from surface water sources such as rivers and lakes.

UNICEF

Status: open, 11 May 2026

Problem statement:
Can we produce a definition of AGI that is constitutive rather than behavioural?

What is AGI, such that we’d know whether we’d built one? The current frame treats AGI as a benchmark target — pass enough tests, demonstrate enough capabilities, and the label applies. This is the wrong shape of answer because it makes AGI a moving threshold rather than a category. Every time systems pass an old benchmark, the benchmark gets revised, which means the benchmarks weren’t tracking what AGI actually is; they were tracking what we hadn’t built yet. The problem is that the field is operating without a constitutive definition, only with provisional behavioural proxies, and the proxies keep failing in the same direction.

Conjecture: AGI is a Universal Explainer

Status: Open, 11 May 2026

Problem statement:
What kind of knowledge is business knowledge, really? And once we answer that honestly, what should a founder actually do?

Conjecture: This Culture, Now: Why Some Businesses Last

Status: Open, 08 May 2026

Problem statement:
How would you convert billions of dollars into actual impact on the future of AI?

Dwarkesh Patel held a competition looking for solutions to four problems. One of them was:

With OpenAI’s new raise at an 852B USD valuation, OpenAI Foundation’s stake is now worth 180B USD. Anthropic’s cofounders have pledged to donate 80% of their wealth. Nobody seems to have a concrete idea of how to deploy 100s of billions (soon trillions) of wealth productively to “make AI go well”. If you were in charge of the OpenAI Foundation right now, what exactly would you do? And when? It’s not enough to identify a cause you think is important, because that doesn’t answer the fundamental problem of how you convert money to impact. Identify the concrete strategy you recommend pursuing.

This problem was compressed to the problem statement above.

Conjecture: AI and the Static Society: What AI Wealth Should Defend