När når vi AGI?

. How We Understand This Era: The Ceiling of Intelligence Is Being Rewritten

If there is one thing we have learned over the past twenty years, it is this:The greatest commercial opportunities never lie in minor adjustments to products or business models. They arise when the ceiling of intelligence itself makes a leap.This is our most fundamental judgment about the current AI transformation, and it is the idea we most want to convey.At its core, this transformation is not merely a product innovation or a business-model innovation. It is a technological revolution that has raised the very ceiling of intelligence.Whoever can push that ceiling even one inch higher will be able to redefine the boundaries of what thousands of industries are capable of achieving. That single inch is precisely what the new generation of AI companies grounded in first principles are competing to secure.The evolution of intelligence follows a clear path.Artificial intelligence is now completing the transition from perceptual intelligence to cognitive intelligence. Machines are no longer limited to “seeing” and “hearing.” They are beginning to “understand” and “reason.”The next step points directly toward AGI.We have a simple but demanding definition of AGI:AGI is not the intelligence of a single genius. It is the aggregate of all human intelligence.

The great wave has arrived, and the trend is irreversible.Zhipu intends to be the one who faces the oncoming wave and keeps reaching higher.Anything short of the summit is failure.This time, the height we seek to reach is one that belongs to all humanity.Tang Jie Founder of Zhipu AIJuly 11, 2026

The picture holds the entire idea. On the left, standard DeltaNet multiplies the previous state by a single scalar decay factor. Every hidden dimension forgets at the same rate, which is a blunt instrument. On the right, KDA replaces that scalar with a vector, turned into a diagonal matrix, so each dimension in the recurrent state gets its own independent forgetting rate. In notation, the update goes from multiplying the prior state by a scalar to multiplying it by Diag of a vector. That is a small change on paper and a large one in practice: a channel holding a critical long-range fact can hold it, while a channel tracking something local can flush and reuse itself.

Keynote Address by H.E. Xi Jinping
President of the People’s Republic of China
At the Opening Ceremony of the 2026 World AI Conference and
High-Level Meeting on Global AI Governance
Shanghai, July 17, 2026 Distinguished Colleagues and Guests,
Ladies and Gentlemen,
Friends,
Seventy years ago, a group of young scholars proposed the concept of artificial intelligence (AI) for the first time at the Dartmouth Workshop in New Hampshire of the United States. In the subsequent 70 years, AI scientists and researchers from around the world ventured into this unknown territory, forged ahead through twists and turns, and made breakthroughs with persistent hard work. Seven decades later today, amid the new wave of AI development, we are gathering by the Huangpu River to discuss how to promote AI globally for the positive, for good and for humanity. All this makes our meeting highly important. On behalf of the Chinese government and people, I would like to extend a warm welcome to you all.
In the course of history, the invention of the steam engine heralded the industrial civilization, the widespread access to electricity brightened up modern society, and the birth of the Internet brought the entire world together. Each of these technological revolutions has profoundly reshaped our way of work and life, and enabled a giant leap in economic and social development.
Today, major changes unseen in a century are accelerating across the world. The new round of technological revolution and industrial transformation is advancing at a faster pace, and the world has entered an unprecedented period of active innovation on AI technologies. Intelligent connectivity, human-machine collaboration, cross-sector integration, joint creation and sharing, and other intelligent technologies are unleashing enormous power. All this carries within it great opportunities as well as challenges to governance. We human beings must answer the questions posed by our times: How to get along with thinking machines? How to ensure security when algorithm is part of decision making? How to tackle ethical challenges by technologies through adaptive governance? How to realize AI for all when the divide keeps widening? These questions demand serious consideration and real answers from the whole international community.
In China’s view, all countries should take a people-centered approach and develop AI for the positive and for good. We should ensure that AI is an important driver for shared prosperity and common security. We should join hands to build a just and equitable system for global AI governance. To this end, I wish to share four observations.
First, we should adhere to the principle of openness and win-win and boost innovation-driven development. As a new engine of world economic growth and an accelerator for the shift of growth drivers,

For those skeptical of further AI progress: here is why, even without another foundational breakthrough, and even if base-model reasoning improved only modestly from today, we should still expect substantial gains in economically valuable capability.
Current models are often evaluated as though the model alone were the complete system. But on structured tasks, the same underlying model can often move from near-total failure to very high performance when placed inside a well-designed scaffolding and harness.

We're currently in the MS-DOS era of AI "operating systems". A large amount of present-day capability remains poorly elicited.

In the future, with algorithmic improvements and vastly better AI processors, advanced AI systems will be able to run hundreds of Fable-level models in parallel at almost no cost.

These agents will have memory and be wrapped in many layers of formal verification and feedback loops while having access to a large number of fine-tuned models, expert systems, simulators, and tools.

Think of it like this: a kernel of artificial intuition (the LLM model that forms the basis of the operating system) generates thousands of candidate solutions based on user input. A formal evaluation harness selects the fittest solution. The surviving programs are then mutated. Repeat.

Such systems will significantly increase economic usefulness even without better raw reasoning because reliability will increase dramatically.

However, raw reasoning will also increase. Due to their nature, these operating systems will generate enormous amounts of high-quality synthetic data. Think of it as artificial gold panning: compute is transformed into high-quality data through evolutionary search, pruned by artificial intuition, and selected via formal verification. Successful solutions and their thinking traces can then be distilled back into future models, making them better at tasks that previously required expensive external search. Repeat.

As you can see, AI progress will continue no matter what. And further foundational breakthroughs are likely, given that many of the world's smartest people are thinking about how to improve AI, supported and driven by huge economic incentives.

ChatGPT Sol Ultra is amazing, we just keep exploring. Now within 2 hours it disproved one of my conjectures, with an explicit counterexample. This is about the growth of operator entanglement in quantum circuits. Previous OpenAI models never found anything useful in this problem... I will write a blogpost about this soon. And we have our "monster" conjecture, a different topic, existence or non-existence of certain integrable spin chain models. Here Sol has been working almost uninterrupted since sunday. (!!!!) First with High setting, now with Ultra. 105 agents used in total so far, with completed tasks. Continuously exploring different directions... it seems a breakthrough is still missing. But it keeps working. Now I had to stop it for a while, because I want to keep working on the operator entanglement topic further. But we will return to the "monster" problem.

Can I publish a proof that I don't understand? Should I? .... I never thought I would reach this dilemma so soon, but I did. In the last 2 days I played around with ChatGPT 5.5, I reconsidered one of my old problems. It was always too difficult for me, technically. Now AI claims it solved it... This is a problem in stochastic dynamics, probability theory. It needs special skills that I do not have. There is a stochastic system which is complicated. Particles hopping on a 1D lattice, with some special rules. The model is physically interesting, and in good variables it becomes much simpler. One needs to connect the quantities along multiple coordinate transformations, non-local maps, eventually arriving at the desired result. I knew what could be an initial strategy towards a proof. In fact we have the required coordinate transformations in a published paper. But I was never enough to do the full rigorous proof. Now the AI claims it did it for me. It supplied another connection to existing literature, a connection I wouldn't have been able to make. And now the proof seems to be complete. Now I have a manuscript, which is >30 pages long. I worked a lot on the introductory Sections, which explain the strategy. This takes up 10 pages approx. Then comes the grinding, for 20 pages. What did I learn from this? What does the community learn? Should I put this to the arxiv? Or to my blog? Should at least announce the result in a conference or somewhere? Or just tell people in person? Eventually we could try to formalize it in Lean. But I am not sure whether we would learn anything from it. OK then people could say they believe it. If I send this to a journal, the referee will not work through 20 pages of hard grinding. They can give it to an AI if they want to. Then we have the future, where AI referees the work of an AI. In any case, I understand the problem and the proof strategy. Maybe that is actually enough in this case. I honestly don't know.