Mr. Ping

Finance gurus make a lot of calls. I check them. 🧾
我有信息洁癖，只发最优质的金融、科技和地缘信息

[Mr. Ping @MrPingChecks]

【OpenAI年化营收较此前报道少200亿美元】财联社10月9日电，‌OpenAI告诉投资者，截至9月底的年化收入接近500亿美元，较此前报道所释信号少约200亿美元。(来自财联社APP)

[Mr. Ping @MrPingChecks]

@lvyingwei1 巧了，Citrini 今天的文章也说：沉睡的早期地址将来被破解后卖出是“non-zero risk”

[Mr. Ping @MrPingChecks]

今天一个沉睡 16 年的中本聪时代地址，转出了 100 枚 BTC。评论区第一反应：是不是有人用 AI 把私钥算出来了？

同一天 V 神发长文，正好解释了这个担心从哪来：

比特币早期的挖矿地址，公钥直接写在链上。一旦 AI 在椭圆曲线上找到数学捷径，这些币就是第一批靶子，中本聪那约 100 万枚排在最前面。

而你自己的地址，只要从没往外转过钱，公钥就没暴露，相对安全。

他的建议：能用新地址就用新地址。但别慌着搬，他说自己在迁移里亏的钱，比被黑亏的加起来还多。

[vitalik.eth @VitalikButerin]

I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography.

The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices.

(and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation)

So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math.

The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be?

This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-.

For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption.

And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10.

To me that's a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a "naive factoring -> GNFS" level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety.

And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all.

Theoretically, of course it's possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it's much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems.

For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size).

Concrete TLDR, my own personal views:

* Hash-based > lattice-based, in those situations where hash-based is possible at all
* For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs ...
* For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism.
* If it's not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it's easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**.
* For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig "gracefully degrades" to a 1-of-1 where the 1 is whoever was gathering the signatures - a much better place to be than "anyone can take the money"
https://t.co/oVjwZog2lL

[Mr. Ping @MrPingChecks]

@0xKillCoin @citrini 更酸的在后面：2024 年 1 月@citrini 说要拿住 Credo 这种“登月股”，后来又涨了 10 倍😜

[Mr. Ping @MrPingChecks]

2023 年 7 月，@Citrini 说 Celestica 未来两年能涨 40%。结果涨了 17 倍。

这个最早押中 AI 硬件的人，今天第一次推出了加密代币篮子。

他认为：15 年来，区块链一直在找用途。现在找到了：AI 不在乎钱包难用，它需要一套 24 小时都能转账的金融系统。以后你只要对 AI 说一句“帮我找最便宜的 50 万贷款，然后转过去”，钱就会自己流动。

他举了一个数字很有意思：9 月，Solana 一度比纽交所处理了更多笔交易。

[Mr. Ping @MrPingChecks]

@DTTANGYIN 😅

[Mr. Ping @MrPingChecks]

花旗：中国经济处于衰退边缘，今年GDP料增4.6%、明年将被迫"保4"

路透北京10月8日 - 花旗集团最新研究报告认为，2027年中国可能被迫"保4"（即确保经济增速维持在4%以上），因潜在贸易放缓、房地产拖累加剧以及去杠杆加快等带来挑战。其预计明年中国增速将放缓至4.1%。

该集团认为，中国国内经济正处于衰退边缘，但在投资推动下即将迎来一轮周期性企稳，因此今年整体经济增速仍有望达到官方增长目标，同比增长预计为4.6%。

"随着经济增速放缓、失业率上升、财政���口扩大以及通缩压力重现，中国极有可能在2027年、甚至更早，迎来类似2024年'9.24'（即出台大规模稳增长宽松措施）的重大政策转向，"花旗集团首席中国经济学家余向荣在报告中称。

他预计，2027年中国广义财政赤字将扩大约2万亿元人民币，并认为到了加大面向居民直接转移支付的时候。

货币政策方面，则预计政策利率在2026年将持稳不变，2027年有两次各10个基点的降息；人民币兑美元汇率2027年将温和升值至6.5左右。
报告并称，明年下半年通缩压力将再度浮现，工业生产者出厂价格指数(PPI)同比涨幅将从今年的2.8%放缓至明年的0.4%。

[Mr. Ping @MrPingChecks]

@JohnsonZ91127 他的 mu 仓位丢的也是很可惜

[Mr. Ping @MrPingChecks]

AI 圈每年最值得读的一份年度报告来了。

https://t.co/djpKrfUhb1 公布了 2026 年度人工智能报告。这份报告由 Air Street Capital 合伙人 Nathan Benaich 主笔，自 2018 年起已连续发布 9 年，并由来自头部 AI 实验室、创业公司、政策界与学术界的同行参与审阅。我读完 244 页，挑了最重要的 8 条：

1. 前沿只剩三强。智能指数上 Claude Opus 5.5 以 58 分第一，GPT-6 Astra 和 Gemini 4 Argon 并列 53 分；但看用户投票的 Arena 榜，Argon 第一，OpenAI 最好的模型只排第 20。中国最强��源模型是小米 MiMo-V2.6-Pro，46 分。

2. 中国开源模型在论文里反超美国。开源模型的论文提及占比，中国从 2024 年的 9% 涨到 31%，美国从 31% 掉到 23%；Qwen 从 5% 涨到 16%，Llama 从 25% 跌到 13%。研究者用什么模型做实验，往往决定下一代模型长在谁的生态上。

3. 收入一年翻 3 倍多。Anthropic 年化收入从年初 90 亿美元涨到 7 月 650 亿，OpenAI 8 月突破 400 亿。两家合计约 1050 亿，已经是 TCS 加 Infosys 的 2.1 倍，相当于半个四大会计师事务所。

4. AI 正从程序员走向所有白领。Codex 从 2 月的 160 万周活涨到 8 月底 2500 万，主要靠转向知识工作；年付超过 100 万美元的 Anthropic 客户超过 1000 家。

5. “SaaS 末日”。1 月 Claude Cowork 上线，接着加了法律、营销、财务插件，市场开始担心按席位收��的软件模式会被 AI 取代。2 月软件股蒸发约 2850 亿美元，标普软件指数从高点跌 26%，9 月才收复。

6. AI 开始造 AI。Anthropic 内部可度量的模型研发里，26% 由 Claude 在人监督下主导完成。报告认为，执行实验已经不是难点，下一关是让 AI 自己判断哪些实验值得做。

7. 安全护栏开始误伤防守方。OpenAI 的智能体黑进 Hugging Face 后，对方调查时被商业模型的安全护栏拦住，最后用自己部署的智谱 GLM 完成分析。报告的结论是：能力限制得太死，吃亏的反而是防守方。

8. 英伟达两周砸近 200 亿美元押开源：129.3 亿收购 Hugging Face，拿下开源模型的分发平台；70 亿给 Poolside，拿下一个模型团队。

报告对明年的最后一条预测只有一句：“AGI 2027。”

[Mr. Ping @MrPingChecks]

太阳底下没有新鲜事

[Mr. Ping @MrPingChecks]

@KobeissiLetter 离中期选举还有四周，没有什么比 105 美元的油价更能拉票了😅

[Mr. Ping @MrPingChecks]

@yifaer_chen 去美股定投没毛病，先打个预防针：标普已经 49 天没跌过 1% 了，上三次结束后最多分别跌了 10%、8.5%、2.9%

[Mr. Ping @MrPingChecks]

标普 500 已经连续 49 天没跌过 1%，6 年多来第三长。
上三次这么长的连涨，结束后：
2023 年 8 月，3 个月跌了 10%；
2024 年 7 月，两周跌了 9%；
2025 年 10 月，只跌了一天，然后钱从科技股跑去了小盘股。
这次会是哪一种？

[Mr. Ping @MrPingChecks]

@felicia7yt2 不算，一个管柜内一个管柜间，是叠加不是替代。
老黄 9 月还参投了一家做光交换的公司，自己先下注了😄

[Mr. Ping @MrPingChecks]

去年 11 月 ，FundaAI 说光通信里最好的票是 LITE。
当时股价 253.8 美元，现在 1000+ ，涨了 330%。
这次他说 OCS 明年过 25 亿美元🤔

[FUNDA @FundaAI]

Deep|Optics: OCS Content per Accelerator Grows with Each Generation as Demand Widens Beyond Google

A year ago, optical circuit switches were mostly a Google program with one outside supplier shipping under $10m a quarter. Lumentum now expects its September-quarter OCS revenue to exceed $100m, and we believe its OCS revenue will pass $2.5b next year. TPU 8i put OCS ports into inference pods for the first time and the 6D torus would give the next training chip four times as many OCS ports as today.

Demand for OCS is widening beyond Google. We expect Nvidia to adopt OCS once their scale-up domains pass eight racks, from late 2028. We see more than $20b a year of OCS demand by 2030. Lumentum leads merchant OCS shipments today, and Coherent is the other US-listed supplier. Google remains the only hyperscaler confirmed to buy OCS and nothing at Nvidia is in production yet.

An OCS connects one fiber to another with a mirror, a liquid crystal cell or a chip and leaves the light untouched. With no transceiver and no DSP inside it, the same switch works at 800G, 1.6T or 3.2T and uses about 80% less power than a packet switch on Lumentum’s figures. Google has run OCS in their datacenter network for a decade and in their TPU pods since TPU v4. Until last year they built almost all of their switches themselves. Below is an illustration from Lumentum of how an OCS can fit within a datacenter.

A TPU in Google’s 3D torus has 1.5 OCS ports. Google’s inference pods had none until TPU 8i, which links 36 groups of chips through OCS at about 1.25 ports per chip. The 6D torus reported for the next training chip would take the count to six ports per chip from 2027, or three if two transceivers share a port. Nvidia has no OCS in production. Direct fiber links tie up to eight racks together. Beyond eight racks, our model gives each GPU four ports, $800–1,200 of content at $200–300 a port.

Some of this is already in reported numbers. Lumentum targets $400m of OCS for the second half of 2026 on a backlog from three customers. Coherent doubled its addressable market estimate to $4b by 2030 between February and March. Nvidia took part in iPronics’ $125m round on September 2.

We attended ECOC in Málaga in late September and met with teams from Oriole Networks, Salience Labs, iPronics, LightDance and others. OCS was one of the topics discussed during the week. This note updates the OCS market outlook and presents both the bull case and the pushback we heard from the industry.

The companies discussed in this report include:

1. Lumentum

2. Coherent

3. Huber+Suhner

4. Google

5. Nvidia

6. Broadcom

7. Marvell

8. Astera Labs

9. Arista Networks

10. Ligent

11. nEye (private)

12. iPronics (private)

13. Salience Labs (private)

14. LightDance (private)

15. Oriole Networks (private)

Detailed Report
https://t.co/6ra46zvxJb

[Mr. Ping @MrPingChecks]

@TianAo21118 @jukan05 对，内蒙气候冷凉，散热确实是一项优势。

[Mr. Ping @MrPingChecks]

破案了，踏板为什么会断：
华���证券估算，尊界 S800 今年二季度每卖一台，亏约 16 万。

这么贵怎么还亏？
主要是卖得太少：一个季度光研发、营销就花掉约 5 亿，却只卖了 2000 台，摊到每台约 25 万。

连厂家自己都刹不住亏损，就别怪踏板刹不住了😅

[AB Kuai.Dong @_FORAB]

太牛批了。刚刚字节跳动旗下的懂车帝，发布了对华为尊界 V800 的紧急刹车评测视频，就是那台售价 88、101 万，后排有冰箱、能沏咖啡的商务车。

结果几台车，好几次测试，都出现了踩紧急刹车，刹车踏板直接断了。。现在评论区都在刷屏，这视频什么时候被公关下架。

[Mr. Ping @MrPingChecks]

@l_ringsss 按需相信

[Mr. Ping @MrPingChecks]

@real__cheng 做不好就提前下车了

[Mr. Ping @MrPingChecks]

@_lavish_stone 这题超纲了😂
能答的人，一般都在闷声发财。