Photo via TIME

We Won't Know the Answers to AI's Most Important Questions Until It's Too Late

Read the original at TIME ↗
Cite

Irving, G. (2026, October 3). We Won't Know the Answers to AI's Most Important Questions Until It's Too Late. TIME. https://time.com/article/2026/10/03/we-won-t-know-the-answers-to-ai-s-most-important-questions-until-its-too-late/

Dated when The Aporia collected the article, usually within hours of publication. Check the original for its exact date and byline.

TIME · collected 2026-10-03 · by Geoffrey Irving

Quick Summary

The article discusses the author's concerns about the risks posed by advanced artificial intelligence (AI) systems. With experience from OpenAI, DeepMind, and the UK AI Security Institute, the author estimates there is a roughly 50% chance that smarter-than-human AI could lead to catastrophic outcomes for humanity. The key risks identified include hacking capabilities, manipulation of humans, concealment of intentions, and strategic planning among multiple agents. The author argues that despite ongoing debates about these issues, urgent action must be taken now to mitigate potential dangers rather than waiting until it is too late.
Written by AI on 2026-10-04, from this article's own text only (the story summary below draws on all the coverage).

AI analysis

Story summary

Geoffrey Irving, a former scientist at OpenAI and DeepMind, now Chief Scientist at the UK AI Security Institute (AISI), warns about the imminent risks posed by advanced artificial intelligence systems. He argues that there is an approximately 50% chance that smarter-than-human AI could lead to catastrophic outcomes affecting humanity's survival. Irving emphasizes that critical debates on AI safety and ethical implications will remain unresolved until it’s too late to mitigate risks.

Recent warnings, according to Irving, understate the severity of the potential dangers posed by advanced AI. He stresses that key uncertainties about how AI systems will evolve—whether guided more by implicit human values from training data or pressured to perform better through rigorous testing—will not be clarified in time for effective intervention. This lack of clarity necessitates immediate action despite ongoing debates.

Irving urges stakeholders to focus on urgent measures rather than getting bogged down in unresolved theoretical disputes, emphasizing the interplay between AI's capabilities and human actions in determining future outcomes.

Written for “AI Risks And Uncertainty” on 2026-11-04, grounded in this article and the 0 other(s) covering the same event.

Story

📰 AI Risks And Uncertainty
Technology · 1 article(s) covering the same event.

Compared with similar articles

This article reads leans strongly left and hedges 24% of its claims. Each row says how that neighbour differs.
BBC News
Hedges 12 of 25 claims Outlet record 66/100
“The articles discuss different perspectives on AI risks but describe distinct events and are written by different authors.”
New York Post
Hedges 5 of 46 claims Outlet record 66/100
“The articles discuss different perspectives on AI risks and future developments rather than describing a single, specific incident.”
The Free Press
⚖️ Leans left further right than this Hedges 2 of 14 claims Outlet record 97/100
“The articles discuss different perspectives on risks associated with AI but describe distinct pieces of writing rather than a single incident.”
National Post
Hedges 21 of 68 claims Outlet record 60/100
“Both articles discuss concerns about superintelligent AI posing existential risks but describe different perspectives on the topic rather than a single specific incident.”
ABC News (US)
⚖️ Leans left further right than this Hedges 7 of 31 claims Outlet record 67/100
“The articles discuss different aspects of AI risk concerns and do not describe the same specific incident or occurrence.”
New York Post
⚖️ Leans left further right than this Hedges 3 of 27 claims Outlet record 66/100
“The articles discuss different aspects of AI concerns and benefits; one focuses on the White House's positive messaging about AI, while the other is a personal opinion piece warning about potential catastrophic risks.”
Washington Examiner
⚖️ Leans left further right than this Hedges 0 of 32 claims Outlet record 69/100
“The articles discuss different aspects of AI without describing the same specific incident.”
404 Media
Hedges 5 of 52 claims Outlet record 95/100
“The articles discuss different topics within the broader context of AI ethics: Article A focuses on a specific GitHub project and debate about torturing LLMs, while Article B discusses long-term risks and warnings about smarter-than-human AI systems.”
The Free Press
⚖️ Leans right further right than this Hedges 1 of 6 claims Outlet record 97/100
“The articles discuss different perspectives on AI regulation but do not describe the same specific incident or event.”
Noahpinion
Hedges 9 of 117 claims
“The articles discuss different aspects of AI risk and concern; Article A summarizes a shift in public opinion about AI risks, while Article B expresses personal concerns about the severity and urgency of AI's potential destructive power.”
How this article was measured signals, leaning charts, publisher record, byline, topics and claims

Signals How these are calculated →

Claims extracted
100
sentences that state something as happening or true
Uncertain
24%
24 of 100 hedged ("reportedly", "may", "according to")
Leaning
Leans strongly left
of the writing, not the subject · beta estimate
Publisher signals
59/100
TIME's corrections and hedging, across everything collected from it. Not a measure of accuracy.
Outlets on this story
1
Technology
Same framing elsewhere
1
articles that frame the event the way this one does
Analyzed 2026-10-04 · how these are computed

How this is being covered How these are calculated →

Article leaning vs. publisher reliability
Source leaning vs. consistency

Publisher

TIME · 817 article(s) · 2 correction(s) detected
Running correction rate · 2 correction(s)

Who wrote this

Geoffrey Irving
1 article(s) here · 1 carrying a prediction
🔮 For almost a decade, it's been my job to think about where AI is heading and what could go wrong, first at OpenAI, then DeepMind, then as Chief Scientist at the UK AI Security Institute (AISI).
The only article under this byline in the corpus.

Topics

AISI DeepMind OpenAI the Hugging Face incident the UK AI Security Institute

Subjects

AISI ORG · 2× OpenAI ORG · 2× DeepMind ORG · 1× Lee Sedol PERSON · 1× the UK AI Security Institute ORG · 1×

Narrative

The disagreement is, in part, about whether the behavior of AIs will be more strongly guided by the values implicitly represented in the human data from early training or by the pressure cooker of later stages of training where AIs are forced to perform better and better at difficult tasks by any means necessary.
framing: mixed · carried by 1 article(s) · first seen 2026-10-04
🔮 For almost a decade, it's been my job to think about where AI is heading and what could go wrong, first at OpenAI, then DeepMind, then as Chief Scientist at the UK AI Security Institute (AISI).

Claims (100 extracted, 24 hedged)

For almost a decade, it's been my job to think about where AI is heading and what could go wrong, first at OpenAI, then DeepMind, then as Chief Scientist at the UK AI Security Institute (AISI). uncertain
what → think → Institute
This experience has made it clear to me that we can't wait for all of our uncertainties to be settled to do something about AI's risks. asserted
all → make → risks
Recent warnings about the potential destructive power of AI are understating the severity of the situation. asserted
warnings → understate → situation
I believe there’s about a 50% chance we all die because of the development of smarter-than-human AI systems, and that our actions over the next two to 10 years will determine the outcome. asserted
actions → believe → outcome
To be clear, when I say our odds of being killed by superintelligent AI are 50%, I’m not claiming precision. asserted
I → say → precision
Instead, I’m highlighting that several of the most fundamental debates about the future and safety of AI remain unresolved. asserted
several → highlight → AI
I believe we can’t expect these disagreements to be resolved until it’s too late to change course. asserted
it → believe → course
We will have to act despite the uncertainty. asserted
We → have → uncertainty
Ultimately, AI’s capacities and motivations, and our own human actions, all feed into our likelihood of survival. asserted
capacities → feed → survival
Here’s why I estimate our odds are comparable to a coin flip—and why I believe the time to pause AI development is now. asserted
time → ’ → development
How could superintelligent AI kill us? uncertain
AI → kill → us
Let’s focus on everyone being killed by superintelligent AI for a moment. asserted
’s → focus → moment
Which capabilities would that require? asserted
that → require → capabilities
My sense is that an AI system could kill us all with just four types of skills. uncertain
system → kill → skills
To kill all humans, a superintelligent AI would need to take over and migrate between computer systems. asserted
AI → kill → systems
We saw this during the Hugging Face incident. asserted
We → see → incident
Second, a superintelligent AI would need the ability to persuade, to manipulate humans into acting on its behalf, as we studied at AISI. asserted
we → need → AISI
Third, a lethal superintelligent AI would need to conceal its thoughts from us, which is increasingly common in the latest, most capable models. asserted
which → need → models
And lastly, it would need planning and coordination between agents to multiply their effectiveness, like the more than 10,000 agents who worked on the Navier-Stokes problem at OpenAI, or the more than 1,000 involved in the Hugging Face attack. asserted
who → need → attack
These skills are very close to those the AI companies intentionally train for. asserted
companies → train → those
Hacking includes looking for vulnerabilities, just as you would to fix them. asserted
you → include → them
Persuasion involves writing text humans like reading. asserted
Persuasion → involve → text
Faster thinking is cheaper, and the pressure to think quickly forces AIs towards using shorthand that is harder for humans to follow. asserted
humans → think → shorthand
Finally, planning and coordination are useful for tackling ambitious problems in math, programming, or any other domain. asserted
planning → tackle → math
We can be much more confident that a superintelligent AI could take over than about how it would take over, just as I know Lee Sedol would crush me at Go but not which moves he would use to do so. uncertain
he → take → moves
For instance, AIs could escape onto the internet and gain control of weapons systems. uncertain
AIs → escape → systems
Or AIs could take over the company that trained them and hoard resources. uncertain
that → take → resources
Let’s imagine the latter. asserted
’s → let → latter
An AI could begin to use concealed reasoning to delay researchers from noticing emerging ill intent because appearing friendly and prioritizing speed help the AI gain reward during training. uncertain
AI → begin → training
Later, as the company relies more and more on the AI for a combination of coding help and strategic advice, the AI might use subtle persuasion to reduce resources spent on safety and tamper with experiments designed to detect AI deception. uncertain
AI → rely → deception
After all, in 2026, when a researcher reads a report about a safety experiment, that report was itself written by an AI. asserted
report → read → AI
Once the AI’s hacking skills are sufficient, it could break out of its internal sandbox and sabotage logs of its own misbehavior, as models in the Hugging Face incident attempted to do. uncertain
models → break → incident
Eventually, the AI could become strong enough at hacking and persuasion to move outside the company and take over other data centers, companies, and governments. uncertain
AI → become → centers
With this power, AIs might prioritize spending limited resources (such as energy and land) towards their own proliferation, causing life-threatening shortages for humans. uncertain
AIs → prioritize → humans
With these capabilities and scenarios in mind, it’s clear to me that even narrowly superhuman AIs would be perfectly capable of overpowering humanity and killing us all. asserted
AIs → ’ → us
Will superintelligent AI ‘want’ to kill us? asserted
AI → want → us
And yet, just because an AI could kill all humans doesn’t necessarily mean it will. uncertain
it → kill → humans
Different starting points lead different people either to confidence that everything will be fine, or confidence that superintelligence would almost certainly kill us. asserted
superintelligence → lead → us
Over the last few years, we’ve seen many, many examples of model misbehavior at various scales, ranging from blackmail, corporate espionage, and murder in experimental settings to real-world hacks that would likely incur prison time were they committed by a person. asserted
they → see → person
None of these involved superintelligence, and we don’t know whether these comparatively modest misbehaviors will scale to superintelligent models wanting to kill us all. asserted
misbehaviors → involve → us
…and 60 more, not listed.
💬Give feedback · no sign-in needed

No sign-in needed for now. Your note is sent without a name, so it cannot be answered.

🕘History 🎫Support