TSLA特斯拉
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历史观点（5 条，最新在上）
- English · 原文Robotics is next. Both deal count and investment amounts are skyrocketing per pitchbook March data (source: a16z) Good thing is: the same AI DC exposure often has cross-exposure to humanoid ramp. Like DRAM/NAND with memory (on humanoid inference/storage) or DFB lasers with photonics (FMCW LiDAR vision/sensing). Right now most exposure is upstream component parts… or programs within large players like $AMZN or $TSLA. So global IPO season H2 into 2027 for pure play humanoids/robotics companies is going to be fun.中文 · 翻译机器人是下一个大方向。 根据 PitchBook 三月份的数据（来源：a16z），无论是交易数量还是投资金额都在飙升。 好消息是：那些同样投资 AI 数据中心的人，往往也与人形机器人量产布局有交叉。 比如 DRAM/NAND 与内存（用做人形机器人的推理/存储），或者 DFB 激光器与光子学（FMCW LiDAR 视觉/传感）。 目前大部分敞口集中在上游零部件……或者像 $AMZN 或 $TSLA 这类大玩家内部的计划。 所以，从今年下半年到 2027 年的全球 IPO 季，对于纯人形机器人/机器人公司来说，将会非常有意思。
- English · 原文Very interesting statement today: $MU CEO predicts a multi-decade memory demand cycle driven by humanoid robots. "Humanoid robots, he says, will require roughly ten times more memory than today’s Level 2+ autonomous vehicles." "And that demand wave is set to begin before the decade is out." Something as well as was "Over time, we expect the value of on-device AI combined with pent-up unit replacement demand to drive memory demand growth" Which is also another trend (Apple Intelligence is currently dog, but I'm sure we'll see innovations with localized/edge AI). Feels like all the industry leaders from $TSM Chairman, $TSLA Elon Musk, to $MU CEO see humanoids as the next major trend so physical AI is probably next. I wonder if the world is going to have enough memory. Or if we'll see enough breakthroughs to shrink memory usage.中文 · 翻译今天有个很有意思的发言：$MU 的 CEO 预测，受仿人机器人驱动，会出现一个持续数十年的内存需求周期。 “他说，仿人机器人所需的存储容量大约是今天 L2+ 级别自动驾驶汽车所需的十倍。” “而且这股需求浪潮预计会在本十年结束前开始。” 他还提到，“长期来看，我们预计端侧 AI 的价值，加上被压抑的换机需求，将共同推动内存需求的增长。” 这其实也是另一个趋势（Apple Intelligence 目前确实拉胯，但我相信我们在本地化/边缘 AI 方面肯定会看到创新）。 感觉从 $TSM 董事长、$TSLA 的 Elon Musk，到 $MU 的 CEO，所有行业领袖都把仿人机器人视为下一个大趋势，所以物理 AI 很可能就是下一步。 我有点好奇，全球的存储供应够不够用。或者说，我们能不能看到足够多的技术突破，把内存的需求量降下来。
- English · 原文Don’t quite think “siphoned off” is the correct term. It’s capex for massive revenue increase or margin increase down the line. $AMZN is probably my favorite hyperscaler right now and example to give. Amazon’s headcount is absurd, like ~1.57M. If the capex goes into automating their workforce with LLMs. Then transitioning into physical AI: - things from self driving (deliveries) - robotics (Amazon warehouses, shipping automation). + revenue increase from building out AWS compute with Trainium and possibly selling chips too with the Neocloud strat. It’s probably the clearest path forward compared to every hyperscaler out there. $TSLA optimus use case targets is extremely broad as a pitch, but Amazon already has a specific reason to scale robotics for internal opex optimization. As for $GOOGL, probably 2nd right now, AI capex was necessary for defending its Google Search moat Gemini from ChatGPT They also have Google Cloud revenue with efficient TPUs + can sell TPUs like Nvidia GPUs. Gemini user volumes keep going up (despite the lack of contention in frontier benchmarks); and AI strategy to be working for ad optimization too. But there’s less clear paths with physical AI stuff ig? Microsoft and Meta are still trying to convince the market why capex is necessary, (we’re kinda seeing that in effect with Meta’s 30%+ Y/Y revenue growth), but doesn’t look like they’re convinced. As for market narratives, Microsoft Maia seems to be behind, their AI development was stunted from OpenAi investments, so sentiment is kinda in the ground. But think that will change down the road like the 180 with Google. I’m sure all the hyperscalers are seeing the leader effect right now: If you have the leading LLM, people will keep using it. That LLM gets smarter from all the training data; and that gap might be structural. Which is why everyone is kinda rushing the buildout right now, but for some the immediate incentives seem obvious.中文 · 翻译不太觉得“抽走资金”这个说法准确。 这是为了之后大幅增加收入或利润率做的资本支出。 $AMZN 可能是我目前最喜欢的超大规模云厂商，也可以拿它来举例。 亚马逊的员工人数很夸张，大概有 157 万。如果这些资本支出是用来通过大语言模型实现劳动力自动化。 然后过渡到物理 AI： - 自动驾驶相关（送货） - 机器人技术（亚马逊仓库、航运自动化）。 再加上通过 Trainium 扩展 AWS 算力并利用 Neocloud 策略可能销售芯片带来的收入增长。 跟所有其他超大规模云厂商相比，亚马逊的路径可能是最清晰的。 $TSLA Optimus 的使用场景目标太宽泛，但亚马逊已经有了具体理由来扩大机器人规模，用于内部运营成本优化。 至于 $GOOGL，目前可能排第二，AI 资本支出对于保护谷歌搜索护城河 Gemini 对抗 ChatGPT 是必要的。 他们还有谷歌云的营收，搭配高效的 TPU，也可以像英伟达 GPU 那样卖 TPU。 Gemini 用户量一直在增长（尽管在前沿基准测试上缺乏竞争力）；AI 策略也在为广告优化发挥作用。 但在物理 AI 方面，路径更模糊，我猜？ 微软和 Meta 还在努力说服市场为什么资本支出是必要的（我们其实从 Meta 超过 30% 的同比增长营收中看到了效果），但市场似乎并不买账。 至于市场叙事，微软的 Maia 似乎落后了，他们的 AI 开发因为 OpenAI 的投资而受阻，所以市场情绪基本上跌到谷底。 但我觉得未来会像谷歌那样来个 180 度大转弯。 我敢肯定所有超大规模云厂商现在都看到了领先者效应： 如果你拥有领先的大语言模型，人们就会一直用。那个大语言模型会从所有训练数据中变得更聪明；而这个差距可能是结构性的。 这就是为什么现在大家都在抢着搞建设，但对某些厂商来说，眼前的激励显然更明显。
- English · 原文(disclosure: I don't own shares of Leaderdrive, I'm just publishing my research for free). For LeaderDrive, it's a material percentage of every humanoid that gets developed. AGIBot and Unitree has only started to scale up recently, and figures has just reached 10k units shipped (agibot). So P/E valuations would be very high currently since humanoids are not in mass production. Markets are forward looking though. If you think Tesla Optimus, Unitree can mass produce tens of millions or hundreds of millions of robots in the next few years. With trillions of dollars flowing into the humanoid industry. If you have 5% market capture of robot that gets made for the humanoid rollout. That's would command a much higher valuation than Leaderdrive's current marketcap. So LeaderDrive was the robotics player I've identified with the most exposure so far.中文 · 翻译（说明：我不持有 Leaderdrive 的股票，只是免费分享我的研究）。 对 LeaderDrive 来说，每开发一台人形机器人，它都能从中拿到一个可观的百分比。 AGIBot 和 Unitree 最近才开始放量，AGIBot 的出货量才刚刚达到 1 万台。所以目前人形机器人还没进入大规模量产，市盈率估值会非常高。 不过市场是向前看的。如果你认为特斯拉 Optimus、Unitree 在未来几年能大规模生产数千万甚至数亿台机器人—— 而数万亿美元正涌入人形机器人产业。 假如你能在人形机器人 rollout 中拿到 5% 的机器人市场份额，那估值就会远超 LeaderDrive 当前的市值。 所以到目前为止，LeaderDrive 是我发现的人形机器人领域里，仓位最集中的那个玩家。
- English · 原文Well $VPG ended up tripling since my thesis post. I got the ASP wrong in my original thesis, was $150 mass production rather than ~$750 midpoint quoted by management. And $TSLA design out risk made me cut concentration. But 3x regardless not too shabby. https://t.co/ksB8ZrnnEg中文 · 翻译$VPG 从我的观点分享到现在，已经涨了三倍了。 当初我那个 thesis 里 ASP 算错了，实际量产价格是 $150，不是管理层说的中点价大概 $750。 而且 $TSLA 设计被踢出的风险，也让我减了仓位。 不过不管怎么说，3 倍也挺不错了。https://t.co/ksB8ZrnnEg