“We can only see a short distance ahead, but we can see plenty there that needs to be done.”
Alan Turing wrote this sentence at the end of his 1950 paper “Computing Machinery and Intelligence.” After proposing what became known as the imitation game and examining objections to machine intelligence, he closed without claiming that the future was fully predictable. Instead, he pointed to research questions that were already concrete enough to pursue.
The line offers a disciplined way to think about artificial intelligence. Long-term forecasts can shape priorities, but confidence usually falls as the time horizon expands. Near-term work—measurement, reliability, security, interpretability, data governance, human oversight, and the study of real-world effects—can improve systems even when larger debates about future capability remain unresolved.
For developers and policymakers, the quote supports iterative responsibility. Systems can be tested before broad deployment, failures documented, affected groups consulted, and safeguards updated as evidence accumulates. Acting under uncertainty does not require pretending that all risks are known; it requires being explicit about assumptions and creating ways to detect when they fail.
Turing’s sentence is neither a promise of inevitable progress nor an argument for delay. It holds curiosity and humility together. Artificial intelligence presents distant possibilities that deserve serious thought, but those possibilities should not obscure the many immediate technical and social problems that researchers, companies, governments, and communities can address now.
Turing used the sentence to close his 1950 paper “Computing Machinery and Intelligence.”
The paper introduced the imitation game and surveyed practical and philosophical objections to the idea of thinking machines. Source: The Turing Digital Archive.
Teams can focus on observable problems such as evaluation, security, reliability, bias, transparency, and human control.
Short feedback cycles make it possible to learn while documenting uncertainty rather than hiding it behind confident forecasts.
Near-term focus should not exclude research on low-probability or long-horizon risks that could have large consequences.
The challenge is to allocate attention across immediate harms, emerging capabilities, and uncertain futures without treating any one horizon as the whole problem.
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