Alliance for AI Humanity

The arrival of AGI can be defined later. The transfer of judgment cannot. 1. “AGI has arrived.” After OpenAI released its new model, GPT-6 Astra, NVIDIA CEO Jensen Huang posted a short, forceful sentence. “AGI has arrived.” Huang wrote that Astra had been trained on “~100K+ NVIDIA Grace Blackwell NVLink72,” noted that only four years separated ChatGPT, o1, and Astra, and added that “400K GPUs” were coming online next. There is no need to reinterpret the hardware count. What matters is the shape of the statement: a steep rise in compute and capability, ending with the declaration that AGI had arrived. I do not want to decide here whether Jensen Huang is right or wrong. His sentence left me with a different question. From where? Where does one have to be standing for AGI to look as though it has already arrived? We are all looking at the same technology, but not from the same place. And perhaps AGI is not, as we have long imagined, a single line that everyone crosses ...

  Capability is not authority. And a human boundary that cannot stop AI before it acts is only a boundary on paper. Bill Gates recently published a long essay about artificial intelligence. Among the many questions he raised, one phrase stood out: “Human Reserved.” The idea is intuitive. Even if AI and robots become capable of doing certain things, society may decide that some of them should remain in human hands. Gates compares the idea to a nature reserve: a place we could develop, but deliberately choose not to because something valuable would be lost. Many people will read that and say: “Of course.” Of course there should be moments when a doctor, not a machine, speaks to a patient. Of course care cannot always be reduced to efficiency. Of course some decisions involving dignity, grief, responsibility, or human relationships should not automatically be handed to an algorithm. My reaction was different. Why now? Not because the idea is wrong. Beca...

Artificial intelligence has spent the past few years looking in the wrong place for its centre of gravity. The conversation has been dominated by models, benchmark scores, context windows, parameter counts, training compute and the recurring question of which company possesses the most capable large language model. That emphasis made sense when access to frontier systems was scarce and when capability differences were large enough to create a genuine advantage. It makes less sense as the field matures. Organisations now have more choices, open alternatives continue to improve, switching costs are gradually falling and the gap between leading systems is narrowing across many practical tasks. The model remains important, but it is no longer sufficient to create a durable moat. The Model Commoditises An enterprise can choose among models from OpenAI, Anthropic, Google, Meta, DeepSeek, Alibaba and other providers. It can use one model for difficult reasoning, another for coding, a smal...

  Artificial intelligence is moving rapidly from an experimental technology into an everyday companion at work, in education and in our personal lives. We ask AI to write, research, analyse, translate, summarise, code and increasingly to advise us. Much of the public conversation has focused on capability, productivity and the transformation of work. Alongside these opportunities sits an equally important issue of responsibility. Every important technology creates ethical challenges alongside economic benefits. Artificial intelligence raises particularly difficult ones because it increasingly operates in areas once associated primarily with human judgement. It can influence writing, hiring, lending, education and healthcare, while also shaping how people interpret information, make decisions and assign responsibility. These concerns are usually discussed in relation to regulators, ethicists and technology companies. Research into how men and women use generative AI introduces a...