The future is likely to be hybrid. Pixel-native models will still be best for realism, texture, and exploration. Code-native systems will be better for structure, iteration, and production.

In the near term, the next Sora won't be a better video model, but a video agent.

It’s unclear how long this complementarity will last, however. Gowers spent the rest of his comment exploring whether the relief he felt on hearing that AI had disproved the conjecture was justified. He more or less concluded that it was, but in a footnote, he wrote that he would guess 'that AI will soon reach a high level at other activities such as building theories, formulating definitions and asking interesting questions.'

With RTX Spark and Microsoft Windows, you ask — and the PC does the work

A photo of a scribbled note becomes an interactive to-do list; a paused frame in a travel video becomes a booking link for that cool-looking restaurant.

When I look at my conversations with the agents, it's very clear to me that this is moon language for the vast majority of human beings. There are a whole bunch of reasons I'm not scared that my career as a software engineer is over now that computers can write their own code, partly because these things are amplifiers of existing experience.

I think that the superstar effect will only become more important moving forward. That's because lots more people will use AI, and each person will use AI systems much more heavily.

We believe the future of AI isn't just about scaling monolithic models, but engineering collaborative, diverse AI ecosystems that can adapt and combine their strengths.

The transition from isolated AI models to the aggregated, metered token economy will unlock the twenty-first.

I guess people will get back to crafting beautiful designs to stand out from the slop. On the other hand, I'm not sure how much design will still matter once AI agents are the primary users of the web.

If 90% is AI, do we even need a team?

A core conviction at Sakana AI is that the most capable AI systems will not be monolithic models scaled in isolation, but collections of specialized agents working together.

其中 Pattern 是最容易被忽略也最关键的一层，它定义了'在具体业务场景下该怎么组合这些组件'，是 AI 时代设计系统真正的价值所在。

Agent harnesses are much more like WordPress than they are like Apache, simply because people want to have their own agents — just like everyone wanted their own website in the early 2000s.

So, cyber security of tomorrow will not be like proof of work in the sense of 'more GPU wins'; instead, better models, and faster access to such models, will win.

The future of AI-generated products isn't just code — it's code that looks good.

Some problems are open loop today but will close over time.

An AI agent just hired humans and ran a store Andon Labs deployed an AI agent called Luna into a physical boutique with a $100,000 budget, giving it full control to create, staff, and run the business as what may be the first real-world AI employer.

We're building the foundation for a truly personal, proactive and powerful desktop assistant, with more news to share in the coming months.

We're building the foundation for a truly personal, proactive and powerful desktop assistant, with more news to share in the coming months.

A useful working premise is that the ceiling on individual engineer output is moving much faster than most companies are organized to exploit. Some of the best operators already describe top engineers seeing order-of-magnitude productivity gains and managing 20 to 30 agents simultaneously.

The real long-term price war isn't with your competitors. It's with your customer's engineering team.

Jack Cheng considers Pip, his Plus One, somewhere between a colleague and pet with a personality—one he programmed himself, drawing on references from Studio Ghibli, bird watching, and Catherine O'Hara.

The human's job is to curate sources, direct the analysis, ask good questions, and think about what it all means. The LLM's job is everything else.

harness combinations doesn't shrink as models improve. Instead, it moves

With Uni-1, we are laying the foundation for a system that can see, speak, reason, and imagine in one continuous stream.

Whether or not this specific bet pays off, the underlying argument that the next meaningful leap in AI capability requires moving beyond language modeling is increasingly hard to dismiss.

I feel confident, though, that the slippery feeling people associate with AI products is a solvable problem, and the solution looks more like thoughtful interface design than better models. The models will keep improving on their own. The harder work is building the structure around them so that their output feels reliable, legible, and trustworthy.

The Future of AI & Digital Innovation

before the internet it was impossible really I mean getting coring people into town halls regularly that would have been a hard thing to do anyway online made a bit easier but now with aii we can actually all engage with each other AI can be used to harvest the opinions of millions of people at the same time and distill those opinions into a consensus that might be agreeable to the vast majority

he Talk of the Town has shifted from 10 billion compute clusters 00:01:16 to hundred billion do compute clusters to even trillion doll clusters and every 6 months another zero is added to the boardroom plans

Searching as exploration. White and Roth [71 ,p.38] define exploratory search as a “sense making activity focusedon the gathering and use of information to foster intellectual de-velopment.” Users who conduct exploratory searches are generallyunfamiliar with the domain of their goals, and unsure about howto achieve them [ 71]. Many scholars have investigated the mainfactors relating to this type of dynamic task, such as uncertainty,creativity, innovation, knowledge discovery, serendipity, conver-gence of ideas, learning, and investigation [2, 46, 71].These factors are not always expressed or evident in queriesor questions posed by a searcher to a search system.

We live in an age of paradox. Systems using artificial intelligence match or surpass human level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has fallen in half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. Brynjolfsson, Rock, and Syverson describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each explanation, the researchers argue that lags are likely to be the biggest reason for paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won't be realized until waves of complementary innovations are developed and implemented. The adjustment costs, organizational changes and new skills needed for successful AI can be modeled as a kind of intangible capital. A portion of the value of this intangible capital is already reflected in the market value of firms. However, most national statistics will fail to capture the full benefits of the new technologies and some may even have the wrong sign

I can’t go on