RawRobot

[dontbesilent @dontbesilent]

我对各个内容平台的一个基础判断

1. 不要在文字社交媒体变现，要在视频平台变现

2. 文字社交媒体的作用是：给视频平台引流、给视频文稿提供素材

3. 不要在平台内做付费内容，比如 B 站充电、X 订阅、抖音专属会员

4. 付费内容要拉到互联网基础设施里面去提供，比如飞书、网盘、微信、邮件、discord

搞流量和变现可以在同一个地方

但变现和交付，最好不在同一个地方

[RawRobot @nbdxe]

超额收益的本质是认知的变现。高频信息差抹平，“提问”与“非共识判断”成为人类新稀缺能力。 AI 投研熵减，任重道远。

[RawRobot @nbdxe]

宏观政策正在从供给侧刺激专项需求侧刺激

购买首套房贴息，意味着房地产需求刺激的启动

财政部长提出五个“更加”，对应国内需求+科技+民生。

“新三样”的光伏与电池产品告别出口退税时代，算是回应了中国冲击2.0

A股将走向内需驱动，刺激落地

[RawRobot @nbdxe]

房贷贴息范围是总价不超过 150 万，面积不超过 120 平，单价大致对应 1.25 万

房价均值超过这个范围的有四个一线城市，杭蓉宁甬苏福夏珠莞等强二线

所以，政策重心是推动低能级low-tier城市房价见底，而不是普涨

结论：贴息让租金回报率高于贷款成本。推动全国房价见底。估计到金三银四

[RawRobot @nbdxe]

我无法理解加速或暂缓论调

因为这个讨论毫无意义

AI终将在解决问题的赛道上狂奔

一骑绝尘

[OpenAI @OpenAI]

We’re releasing a broad range of new mathematical results produced by an internal frontier model.

We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.

https://t.co/7N6TPlft1P

[RawRobot @nbdxe]

人的工作价值，是因为处于“非标”work flow节点

[Ethan Mollick @emollick]

When people talk about AI easily replacing workers, I think about this ethnography of copier repair technicians in the 1990s.

You see how work is complicated and improvised by workers and informal and badly documented.

(But eventually technology did largely eliminate this job)

[RawRobot @nbdxe]

AI 的下一个重大应用将发生在人类语言可以描述，但无法被人脑全面识别的领域。比如生物科技领域的 DNA 和蛋白质的结构数据库

数据量足够庞大，但人类传统手段过于低效

[Freda Duan @FredaDuan]

AI Drug Discovery Is Becoming a Bottleneck Trade

I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments.

Two things are happening:
1/ Upstream: the frontier AI labs are piling in.
2/ Downstream: The supply chain is clearly moving.

Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume.

DNA → protein → assays → sequencing → automation → preclinical testing

Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs.

$TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27.

@GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26.

---
Why does AI drive more wet-lab demand?

1/ AI makes hypothesis generation almost free → way more shots on goal.
The bottleneck is moving from expert-driven design to biological validation.

2/ AI models need continuous experimental feedback — and both good and bad data are useful.
Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data.

@GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback.

@Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration.

3/ Wet labs are no longer just making drugs. They are making training data.
$TWST / @GenScript increasingly look like biological data foundries.

$TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model.

Traditional drug discovery asks: “Does candidate X work?”

AI drug discovery also asks: “What can this experiment teach the model?”

---
TAM of AIDD

If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work.

Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment.

---
Trade setup

This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+)

It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity.

After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand.

?? But clinical trials are still the bottleneck?
This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital.

But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process.

And who knows: clinical trials themselves may eventually be optimized by AI.

?? What breaks the trade?
Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly.

Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much.

?? Milestones
Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox.

2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs.

Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs

+++
More comprehensive analysis: https://t.co/vaTuiyyYB6

[Mario Joos @MarioJoos]

🚨 MAJOR ANNOUNCEMENT: YouTube will start prioritizing original content and reducing re-uploads (RIP clipping)

YouTube has just announced one of the most impactful changes to its short-form algorithm. Its recommendation systems (the algorithm) will start to further prioritize original content and reduce the reach of content re-uploaded from other creators without adding anything of your own.

They highlight this shift toward originality, where channels with primarily re-uploaded content will see less distribution within the Shorts feed.

It also seems like they’re really trying to put the focus on content with real value, while highlighting that YouTube doesn’t want as many “minor technical edits” or “template-based” changes. Which is basically all that clipping was in this industry.

Overall, while this will piss off some people whose entire careers were built on the premise of other people’s content (mostly clippers), the change itself is amazing and will finally address the overall quality issue we’ve been seeing.

[Big Brain Business @BigBrainBizness]

Meta Chief AI Officer Alexandr Wang on why your product can be better and still lose, because perception is more real than reality:

Most founders assume the best product wins. Build something that works, prove it with data, and customers will see it.

Wang says that assumption breaks down when you sell to large organisations.

"Probably a lot of the companies that you all have worked with, they're very data-driven companies. It feels like the truth makes its way... everybody serves a shared sense of reality and whatnot. That's not true at most large companies and also not true within the government, unfortunately."

So what do they run on instead? Perception.

"At a lot of large customers, perception is more real than reality. The reality is just so ugly most of the time that very rarely do people actually confront reality, and most of the time they just sort of choose to believe the perceptions that they live in."

That is how a better product loses. If buyers aren't judging reality, being better in reality isn't enough. The deal goes to whoever owns the perception.

Wang spells out what this means for anyone building or selling to enterprises:

"If you end up doing enterprise sales or you end up building enterprise products... just as much as your job is to improve the reality, it is to shape the perception."

The product still matters, but by his reckoning, it's only half the job.

One company he singles out for mastering the other half is Palantir:

"One of Palantir's superpowers is that they shape the perception better than most other technology companies do, because I think they view themselves... it's like a combination of a sort of acting troupe combined with a software company."

And @alexandr_wang means it literally:

"They literally give an acting book to all of the new hires, or they did for a very long time."

A software company that trained its people in performance. That tells you how seriously Palantir took the perception half of the job.

[Shaun Maguire @shaunmmaguire]

I left Silicon Valley 6 years ago

Why?

Because it's a monoculture

The most extreme group think of any place on the planet

Show up to a dinner party and everyone is in tech

So what do they do? They start stack-ranking each other

And it's a totally ordered set: being founder of co $100B market cap > founder of co with $50B market cap > managing partner of tier 1 venture firm > hired CEO of $50B public co > founder of $10B co > etc etc

Pure toxicity that nerfs creativty

And then as a corollary, people are especially afraid to speak truth to power as everyone knows their place in

"The Hierarchy"

Reject this nonsense. Stay weird. Think independently. Be honest with yourself. Have fun.

[Salonbus @salonbus1024]

金融脱实向虚其实也到了尽头，从全球外包，驱赶蓝领，到互联网化，消灭大部分白领，到量化控盘，不再需要靠谱的商业只需要量化控盘，连高管也过剩了。 最终，AI 与金融合体，成为虚拟经济的集大成者。 孤阴不长，独阳不生。 极致的虚，就是极致的实。从石油到发电机，最质朴的基础工业需求涨疯了。国债收益率的大涨，本质上就是资金从金融市场流出去工业市场，要么AI维持有限规模和有限速度，要么扩大工业和能源投入。

[姚金刚 @yaojingang]

不知不觉，GEO公开课已经完成了5期了

和@vista8 向阳老师在开始讨论系列GEO公开课时，初步定的是每月1期，持续12期，还剩7期

每期定一个大的主题，然后通过直播的形式，完成关于这个主题的系统性分享
之后每次分享，会做个简单的调研，根据大家当前最关心的问题，来调整公开课的内容节奏与安排

下面是前五期直播的图文总结版，欢迎收藏：

1、第一节公开课：《从AI搜索逻辑到GEOFlow落地实战》
https://t.co/6dxewBjFSX

2、第二节公开课：《一文讲透GEO内容工程：目标、要素、关系与反馈》
https://t.co/GCEovIkFkp

3、第三节公开课：《一次讲透GEO信源偏好与信源投放策略》
https://t.co/Sg3qnFfgiZ

4、第四节公开课：《官网正在成为GEO主阵地：从企业事实到AI引用，官网GEO实战公开课》
https://t.co/lWLGbZg9qc

5、第五节公开课：《GEO到底该投多少钱？1.3万字讲透效果归因与ROI（附10种标记方法）》
https://t.co/oX95eA0hrt

[RawRobot @nbdxe]

同感

[AI最严厉的父亲 @dashen_wang]

真的感觉好快。

智能体也要被淘汰了。

[sleepy @sleepy0x13]

美国现在是真把一部分国运押进 AI 机房了。

WSJ 今天发了篇报道，说这一轮 AI 基建潮，正在变成美国历史上规模最大的经济豪赌。

Brookings 最新测算，2025 到 2032 年，美国在数据中心、电力、网络、芯片这些 AI 基础设施上的总投资，可能达到 10.3 万亿美元，平均每年相当于美国 GDP 的 3.63%。

19 世纪美国修铁路，年均 2.24% GDP；
艾森豪威尔时代修州际高速，1.13%；
90 年代铺通信和光纤，1.1%；
美国当年全国搞电气化，也只有 0.5%。

现在 AI，3.63%。

而且这已经开始从科技行业往整个美国经济里渗了。

WSJ 引用的数据显示，五大 hyperscaler，Alphabet、Amazon、Meta、Microsoft、Oracle，到 2029 年四年资本开支预计达到 4.2 万亿美元；AI 从 2023 年到现在已经带来超过 75 万个新增岗位，数据中心从 2024 年以来也新增了 11.7 万个岗位。

与此同时，电工工资在涨，电价在涨，芯片和设备价格在涨，一些地方制造业项目甚至开始跟数据中心抢电、抢人、抢土地。

民主党如果推动 AI Pause 或数据中心 moratorium，就是在砸特朗普经济。

这句话党争味儿很重，民主党内部其实也没这么整齐。AOC、Pallone 等人确实支持过暂停数据中心建设，但民主党领导层里也有人没有跟进全国 moratorium。

不过，AI 现在已经大到，你想给它踩刹车，踩的可能不只是 OpenAI 和 Anthropic，而是美国的建筑、电力、芯片、就业、信贷和资产价格。

反过来也一样。

Brookings 这篇研究专门提醒，现在越来越多 AI 基建开始通过 private credit、SPV、租赁承诺、贷款担保这些表外结构融资。

大家都默认未来 AI 收入能把今天几万亿美元的机房养起来，只要这个故事有一天接不上，掉下来的也不会只是一批 AI startup。

这才是现在最有意思的地方。

模型到底什么时候 AGI 还没吵明白，AI 已经先从科技行业问题，变成美国的宏观经济问题了。

一个行业每年准备吃掉 3.6% GDP，benchmark 已经是里面最不重要的那个数字了。

[孟岩-Mike Meng @myanTokenGeek]

https://t.co/DOUyusGAEf

[will depue @willdepue]

no investor, no startup guy, no counselor, no content creator, no musician will avoid going the way of the software engineer and mathematician

[RawRobot @nbdxe]

太漂亮了

[HEO @heospace]

New view from space!

We captured this image of the Chinese Space Station as it passed over the coast of Hawaii.

At 12 cm/px resolution, we can see the expected T-shaped configuration of the CSS, with its solar array wings deployed and all science modules visible: Tianhe, Wentian and Mengtian.

[Arena Physica @arenaphysica]

Can the US build a DJI? We bought a 2019 Mavic Mini and a 2023 Mini 4 Pro, tore them down in our hardware lab, and put them under a bench microscope to find out.

What we found: https://t.co/2nxcHd8cSF.

[Blackstone @blackstone]

The Industrial Revolution unleashed unprecedented economic activity — an indicator of AI’s potential future impact. 💡 Watch Jon’s full talk: https://t.co/NbHsv7yfbP