智能体手机加速爆发:换机困境、端侧AI与下一代入口之争
硅谷101梳理了智能体手机在2026年加速落地的三股推动力:换机周期拉长、端侧AI算力成熟与端云协同架构成型。文章以努比亚NaviX Ultra搭载豆包手机助手和骁龙旗舰平台为例,实测了长按AI键跨App操作、全局读屏与记忆能力,并对比苹果、谷歌、OpenAI在系统级Agent与超级入口上的不同路线。
硅谷101梳理了智能体手机在2026年加速落地的三股推动力:换机周期拉长、端侧AI算力成熟与端云协同架构成型。文章以努比亚NaviX Ultra搭载豆包手机助手和骁龙旗舰平台为例,实测了长按AI键跨App操作、全局读屏与记忆能力,并对比苹果、谷歌、OpenAI在系统级Agent与超级入口上的不同路线。
短剧领域正出现一种新的职业剧形态,以《急诊摸鱼王》《冒姓琅琊》为代表,把专业知识和专业能力变成主角的“金手指”,而非依赖财富、权力反转。医疗短剧《急诊摸鱼王》呈现急诊分诊、创伤初步评估等专业细节,评论区有医学专业人士用术语参与讨论。这类创作强调专业打底,让观众在快节奏叙事中了解另一个行业。
截至2026年6月,我国日均词元调用量已突破500万亿,Token正成为连接模型服务、推理算力与数据供给的计量接口。DeepSeek自8月起探索峰谷时段定价,9月10日调整Flash系列价格,闲时每百万词元缓存命中输入价0.02元。无问芯穹跨集群异构推理架构在特定测试中首词元等待时间降低51.5%、单词元成本降低37.5%。
2026年诺贝尔经济学奖授予罗伯特·霍尔与迈克尔·伍德福德,表彰其为宏观经济稳定化奠定微观基础。浙大教授史晋川在对话中表示,提振消费不能只靠刺激传统需求,中国居民消费结构正从"住行"转向服务型消费,需通过改革放开养老、游戏等服务领域,让产业满足需求结构转变。
SF Compute 正把 GPU 算力做成可交易市场,并自建云运营能力实现"物理交割",让买家拿到可用算力而非现金结算。CEO Evan Conrad 称 NVIDIA 因缺乏中立性无法自建这类交易所,而 SF Compute 通过匹配不同约束条件的订单在技术快速迭代期维持交易。他还提到可动态切分 InfiniBand 网络等让基础设施可编程的工程细节。
TripleTen 2026 年调查显示,48% 金融从业者被要求使用 AI 聊天机器人,科技业之外医疗仅 18%、教育 25%。79% 的受访员工被要求学习至少一项新技能,但仅 30% 的管理者认为判断 AI 输出对错是必备能力。员工主导的 AI 创新中 88% 成为公司标准做法。
MIT Technology Review 文章指出,OpenAI 的 Dots、Meta 的 Muse 等个人 AI 智能体大规模上线后,已成为人类和机器人之外的第三类网络访客,Muse 首月下载量达 500 万次,8 月至 9 月智能体网络流量增长两倍。
后摩智能 10 月 14 日在北京举办生态合作伙伴大会,圆桌环节五位来自操作系统、应用、数据工程、整机集成和芯片领域的从业者讨论端侧 AI 如何成为一门生意。麒麟软件姚翎称涉密场景下本地安全没有商量余地,罗格数据刘学鹏认为企业要把 AI 数字员工招进内部而非放在云端。后摩智能陈仲铭表示正通过 M 系列做大算力和先进封装做大带宽,突破算力、带宽、功耗的「不可能三角」。
我从这篇帖子中学到的是,几乎没有人是“AGI-pilled”的,他们也没有认真对待这样一种可能性:数千万到上亿个AI以人类10–100倍的速度全天候工作。
Surveyed Anthropic staff's answers imply 17.1% GDP growth in 2030
Anthropic 发布报告称,Z.ai 于 8 月发布的开放权重模型 GLM-5.3 可被任何人自由下载和修改,构成重大网络攻击风险。开放模型支持者认为其可本地运行、成本更低、便于学术研究并防止权力集中,而 Artificial Analysis LLM 排行榜前八名目前仍均为闭源模型。
过去几周游戏 x AI 能力的阶跃式提升是史上最大的 未来一年将发生巨大变化 当然,最好的游戏仍将由充满热情的团队打造
We have a huge pivot underway here at Epic, moving developers around to retool for maximum AI acceleration. The aim isn't to do the same stuff with fewer people, but to enable everyone here to build MORE and BETTER stuff, and to bring the benefits to all teams using Unreal.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.