By Futurist Thomas Frey
For years, I’ve been known as the “unanswerable questions guy”—the person who asks questions that make people pause, scratch their heads, and admit they genuinely don’t know.
I’ve asked questions like “What comes after the Internet?” and “How many galaxies will humans eventually colonize?” Questions that neither science nor religion could definitively answer. Questions about managing systems that don’t exist yet, about unintended consequences we can’t foresee, about futures we can barely imagine.
But something fundamental has shifted. With AI, there are no questions that AI cannot answer. Some answers are speculative, some are wrong, some are poorly reasoned—but there are no “I don’t knows.” Ask an AI anything, and it will give you something.
So what’s the equivalent moving forward? What stumps the world when machines can generate plausible responses to anything we throw at them?
From Unanswerable to Unaskable
The evolution isn’t from answerable to unanswerable—it’s from unanswerable to unaskable. The challenge isn’t finding questions without answers anymore. It’s finding questions we don’t yet know how to formulate.
Before Einstein, you couldn’t ask meaningful questions about spacetime because the concept didn’t exist. Before we understood evolution, questions about natural selection were literally unthinkable. The questions became askable only after someone created the framework.
The person who can identify what questions should be asked becomes more valuable than the person who can answer existing questions.
Questions We Can’t Frame Yet
On existence beyond biology: I used to ask “How long before we can transfer human consciousness to a non-biological entity?” AI will answer that confidently. But the real question—the unaskable one—is about what happens to identity, continuity, and personhood when we exist simultaneously across biological, digital, and quantum substrates. We don’t have frameworks for thinking about multi-substrate existence.
On distributed intelligence: I asked “How many connections will it take before the internet becomes self-aware?” But that assumes awareness is a property of individual entities. The unaskable question is about intelligence that emerges from networks where no individual node is conscious but the system exhibits sophisticated behavior. We lack concepts for distributed agency and emergent intentionality.
On post-scarcity: I’ve asked “What happens to capitalism after the age of abundance arrives?” Everyone takes a crack at this. But we’re asking from inside a scarcity-based framework. Our entire conceptual apparatus—markets, value, trade, property, work—is built on scarcity. We’re like fish trying to ask about life outside water, except we’re actively preventing ourselves from developing the concepts that would let us ask properly.
Questions About Experiences We Haven’t Had
On accelerated cognition: “What does it feel like to think at 1,000x human speed?” AI will describe this eloquently. But the question is inadequate because we’re asking about an experience so fundamentally different from human consciousness that our language can’t capture it. The unaskable question involves forms of experience that become possible when cognitive speed operates at scales our neurobiology can’t approach—and we can’t frame it properly because all our reference points are human.
On interstellar governance: I asked “What happens when we establish colonies that are 4.2 light years away?” AI generates speculative answers. But we’re asking from within a framework of near-instantaneous communication. The truly unaskable question involves conceptualizing political agency and collective decision-making when “now” is meaningless and “consensus” takes longer than human lifespans. We’ve never lived without real-time feedback loops, so we can’t properly imagine governance without them.
Questions That Reveal Our Conceptual Inadequacy
On consciousness: “When does an AI become conscious?” gets debated endlessly. But the question assumes consciousness is binary—you have it or you don’t. The unaskable question is: “What do we do when consciousness turns out to be a multidimensional spectrum where different information processing systems have different kinds of experiences that don’t map onto our conscious/not-conscious binary?”
On moral status: “What do we owe to simulated beings?” Everyone debates this. But the question reveals we don’t have coherent frameworks for moral consideration. The unaskable question is: “How do we build ethical frameworks when we can’t agree on what properties generate moral status, and we’re about to create vast numbers of entities with some but not all of those properties?”
On human meaning: “How do humans find meaning when AI can do everything better?” This gets asked constantly. But the question is trapped in a framework where meaning derives from capability or productivity. The unaskable question involves reconceptualizing meaning and purpose in ways that don’t depend on humans being “good at” anything. We can’t ask it properly because our civilization is built on comparative advantage and being useful.
Questions About Systems That Break Our Categories
I used to ask about the unintended consequences of our technologies. What are the second and third order effects of self-driving cars? Of artificial intelligence? Of life extension?
But now the deeper question is about consequences we can’t anticipate because they happen in conceptual spaces we haven’t mapped. What are the implications of technologies that don’t fit our categories of “tool,” “entity,” or “system”? What happens when the boundaries between human, machine, and network become meaningless?
These aren’t just hard to answer—they’re hard to ask because our language and concepts are inadequate.
The New Role: Question Architect
The valuable skill moving forward isn’t asking questions nobody can answer. It’s asking questions in ways that reveal we need new conceptual frameworks entirely.
The “unaskable questions person” does several things:
- Identifies hidden assumptions in how we frame problems
- Recognizes when existing categories are inadequate for new phenomena
- Points out when we’re solving new problems with old frameworks
- Creates space for developing new vocabulary and concepts
- Makes visible questions we’re avoiding because they threaten existing structures
This is different from the “unanswerable questions guy.” That role was about finding gaps in knowledge. The new role is about finding gaps in how we think about knowledge itself.
Practical Examples
Here are questions that seem askable but actually need better frameworks:
- “How do we maintain privacy when prediction becomes perfect?” (Reveals privacy depends on uncertainty about future behavior)
- “What’s the value of human labor when machines do everything better?” (Reveals we tie human worth to productivity)
- “How do we have democracy when AI can predict election outcomes perfectly?” (Reveals democracy depends on uncertainty)
- “Should we extend life indefinitely?” (Exposes contradictions: we value individual life as sacred AND accept that death is necessary for renewal)
Each gets answered constantly. But the answers are unsatisfying because the questions themselves need reconceptualizing.
Final Thoughts
The era of unanswerable questions is over. AI can generate responses to anything. But that doesn’t make the questioner obsolete—it elevates the role.
The new frontier is identifying when questions themselves are inadequate. When we’re trying to use conceptual frameworks designed for one era to handle challenges of another.
Being the “unaskable questions person” means recognizing when everyone’s asking the wrong questions—not because the answers are unknown, but because the questions need to be reconceived.
It means building the conceptual infrastructure that makes tomorrow’s questions askable. Creating the frameworks that let us think about problems we can’t quite articulate yet.
In a world where AI can answer anything, that might be the most valuable contribution anyone can make: helping us figure out what we should actually be asking.
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