In 2016, typical expert guesses put human-level AI decades out. By the mid-2020s, large surveys and lab leaders had dragged those dates forward, sometimes into the working lives of people reading this. The timeline is still uncertain. The direction of the revisions is not.
What the prediction surveys actually show
The most systematic data on AGI timelines comes from AI Impacts, which has surveyed AI researchers on when they expect to see "human-level machine intelligence" (roughly equivalent to AGI) across multiple years. The results are instructive.
In 2016, surveyed researchers placed the median probability of HLMI at roughly 50% by 2061. By 2022, before the public release of GPT-4, that median had already moved significantly earlier. The researchers making these predictions are the people building the systems they are predicting, not doomsayers or publicists.
The pattern in the data is no agreed target date, but a converging window with little agreement on the precise year. The pattern is that each new survey (calibrated against actual capability developments) produces shorter timelines than the last.
Four years. From text that barely held together to performance in the top decile of human experts on standardized measures of professional competence. This is the empirical record against which timeline predictions must be calibrated.
What the people building AI are saying
The public statements of frontier AI laboratory executives are not the same as their internal working assumptions. But they are notable nonetheless, because public statements by people in these roles are typically conservative, constrained by investor relations, regulatory scrutiny, and the professional norm of caution about extraordinary claims.
"We are now confident we know how to build AGI as we have traditionally understood it."
Sam Altman, CEO of OpenAI · January 2025
"I think we could be just a few years away (maybe even sooner) from AI that surpasses human intelligence across the board."
Dario Amodei, CEO of Anthropic · "Machines of Loving Grace" essay · October 2024
These are the considered public communications of the people making capital allocation decisions based on internal projections that are almost certainly shorter than what they publish, not the statements of people who think AGI is a distant prospect.
When a company spends tens of billions of dollars on AI infrastructure based on an internal assumption that transformative AI is imminent, the timeline is operationally real, regardless of what the PR team says about uncertainty.
Why the date is less important than the distribution
The natural response to AGI timeline discussions is to focus on a specific date: will it be 2027? 2030? 2040? This framing is understandable but misleading. The question that matters for governance is not "when exactly?" but "what is the probability that AGI arrives within the planning horizon of our current institutions?"
Consider what building adequate ASI governance actually requires:
- International treaty negotiations typically take several years from initiation to signature
- Ratification and implementation of international agreements takes additional years
- Technical infrastructure for verification and monitoring must be designed, funded, and deployed
- National legislation in major AI-developing countries must be passed
- Compute governance frameworks require industry compliance mechanisms built over time
Even with extraordinary political will, a meaningful international ASI governance framework built from scratch requires a minimum of five to ten years. If the probability of AGI arriving within a decade is non-trivial (and the evidence suggests it is), then governance that begins after AGI becomes "clearly imminent" will not finish in time.
This is already a present problem. The governance window is neither unlimited nor static. It is narrowing.
The asymmetry that makes this decision easy
There is an asymmetry in the two types of error available here. If we build robust ASI governance frameworks and AGI turns out to be further away than expected, the cost is some wasted effort, some regulatory friction for AI development, and a set of international institutions that stand ready when the moment arrives. If we wait for certainty about timelines before building governance, and AGI arrives before governance is in place, the cost is potentially unrecoverable.
This asymmetry is the same one that motivated nuclear governance before weapons had spread, ozone protection before the ozone layer had collapsed, and pandemic preparedness before the next outbreak arrived. In each case, the institutions built under uncertainty proved their value when the predicted scenario materialized.
"Humanity has a poor track record of building institutions after the fact that required anticipation to build before the fact."
Yoshua Bengio, Turing Award Winner & Nobel Laureate · 2024
The case for acting now does not depend on believing the most aggressive AGI timelines. It depends only on taking the full range of credible estimates seriously (including the shorter ones) and responding to that uncertainty with appropriate governance action.
What comes after AGI
AGI is the threshold of concern, not the terminus. A system at human-level cognitive performance across all domains has the capability to improve its own design, to recursively self-enhance in ways that could compress the timeline from AGI to artificial superintelligence into months, not decades.
This is the scenario that has motivated the most serious warnings from the researchers closest to the technology. Not AGI itself, but the dynamics that may follow AGI, including the possibility of moving from a controllable system to an uncontrollable one faster than human institutions can respond.
The implication for governance is direct: the frameworks must be in place before the AGI threshold is crossed, not after. The time to negotiate is now, while all parties are still building toward the threshold and none has crossed it. The Nakada Foundation's three policy proposals are designed for exactly this window.
The strongest pushback
The fairest objection is that timeline forecasts have been wrong before, so governing on them is panic. Fair about point predictions. The portable fact is the direction of revisions and the asymmetry of being late: institutions need years; capability can jump in quarters. Build the frameworks while the threshold is still ahead.