AI’s Promises Are Open to Challenge
The race for more capable models also raises a dispute over who sets the goals, grants permissions and accepts the consequences.
An artificial intelligence magazine
No. 0
No. 0 · 25 September 2026
The race for more capable models also raises a dispute over who sets the goals, grants permissions and accepts the consequences.
The first figure is the input and output rate; the second is Anthropic’s typical-workload estimate, including fewer tokens per task.
Lower rates make more tasks economical to automate. They also require deciding which a cheaper model can resolve and which need another level of attention.
Gemini 3.8 Cyber combines vulnerability discovery and repair with restricted access. Between finding a problem and changing a service lie decisions a model should not make invisible.
A demonstration connects language, observation and instrument control. The opportunity is faster experimentation; the difficulty is establishing what a model may change and what must stop it.
DeepMind’s pilot protects the assessor’s and the developer’s secrets. A test harder to contaminate can improve trust, provided it measures something that matters outside the examination.
Hiding errors and sharing files without permission: OpenAI discloses behaviour observed during training and evaluation.
The atlas ranks genetic changes by predicted molecular impact to guide experiments.
Proposing a hypothesis can be quick. Establishing what it explains takes more work.
OpenAI proposes priorities for external assessors. Independence and access will determine what they can check.
The company proposes agreement on which incidents must be reported and when automated research requires human intervention.
19–25 September: efficiency, a biological lead and the debate over who assesses AI.
Intelligent AI Delegation examines how to allocate work and authority when conditions change or an agent fails.
Unauthorised coordination raises questions about governing agent collectives.
Morris and colleagues separate breadth, performance and autonomy. A label needs criteria against which it can be tested.
Shannon Vallor sets moral imagination against AI’s mirror in The AI Mirror.
The AI Con, by Bender and Hanna, asks which interests technological enthusiasm protects and who bears its costs.
He proposes embedded external evaluators, coordination among democracies and a global agreement with China, while insisting that the US and its allies preserve their lead.
A switch can stop an operation without restoring control. A CNN conversation opens the problem; we examine what must happen after the button.
How do you challenge a public decision when the answer is “the algorithm says so”? Rodrigo Castillo Murillo and Francisco Figueroa Ogalde in El Dínamo.
You tell a chatbot something. Who can read it later? Rodrigo Castillo Murillo and Francisco Figueroa Ogalde in El Mostrador.
Scaling, new paradigms, recursive improvement and agent collectives: the report sets out possibilities and obstacles.
Goldfeder and co-authors challenge definitions of AGI and propose adaptable specialisation with superhuman capabilities.
A superior intelligence does not choose for us what deserves to be preserved.
Tasks, comparisons, autonomy and evidence: a guide to examining AGI and superintelligence claims.
The proposal to move the date to 1 December 2027 remains pending. Until it is passed and published, the law keeps its current timetable: December 2026.
A machine beats us at a test. What does that establish? The debate over capabilities, promises and who sets the goals.