Elbert Li

最近关于AI Phd的话题在X上讨论很热，pick几个有趣的观点： 1/4 学术界与工业界在时间尺度和成果估值上已经严重脱节，导致许多AI PhD的工作在毕业时可能已失去实际意义 AI对学术界的影响也巨大，一个课题被工业界几个月干出来了，最害怕的是五年后毕业了，然后自己研究的领域在工业界可能已经成一个范式了，可能也没有更进一步的空间了@Lanyi_adict AI人才长期从学术界向工业界集中，转入产业后平均论文数量下降、专利活动显著增加、收入上升，今天必须把AI research拆成Frontier scaling research和Idea-constrained research @Phoenixyin13 作为一名博士，现在越来越怀疑自己到底还有什么独特的优势，工作上大多数事情都是Claude完成的，方案是它提的，问题是它分析的，自己最大的作用有时只是点击推荐选项，这么基础的工作也不需要博士学位@hkleo999 这世上从来就没有绝对的公平和信息对等，对于年轻人来说，很多时候读PhD未尝不是一个理性选择，因为如果不读，也并不代表有其他更好的选择，成年人要做informed decision并承担不确定性@0xcrypto458579 2/4 当前的PhD培养体系过度依赖论文数量与外部激励，正在把研究训练变成资格认证和资源竞争，而非真正的科学探索 一直对着学术圈reward hacking是一个很让人沮丧的事情，但是也不用对工业界赋魅，水平低劣骗融资的公司与水论文乱讲故事实验室一样多@liiiaff 学术界进入“大化缘时代”，不断“Begging” for collaboration, GPUs, API credits, and so on @HaiyangXu3110 现在可能是最不值得读CS PhD的时代之一，CS的重要性和CS PhD的价值完全是两回事，大量学术研究被funding和publication incentives绑架，很多教授为了拿经费不得不向AI靠拢，学生也被迫围绕热门方向制造research stories @exbyte_li AI正在淘汰PhD训练中那些原本被误认为是研究能力的机械劳动，去读博士是为了学习如何判断什么问题值得存在，Don’t do a PhD to learn the tools. Do a PhD to learn how to decide what deserves to exist @Phoenixyin13 博士对大部分人本就是鸵鸟选择，不愿意面对就业压力而已，有多少人是为了真正研究学术问题而去读的博士？把社科类的所有博士加一起，我觉得连5%都不到@Daniel_Cai_1989 三个这两年的身边例子，一个高中学霸本科在lab呆了两年没文章申请了几十所phd全聚德，一个GPA3.9+两个四大顶会非一作也全聚德，最后一个搞ai的本科阶段五篇文章两个一作四顶会也全聚德，结果跑到了一个末流R1去做了半年phd就退学 @Alenjonesyi 3/4 面对现实压力，不少人选择主动转向工业界、调整发表策略或重新评估PhD的机会成本，同时承认工业界也并非理想净土 年初用autoresearch工具配上codex和claude code两天完成一篇中等水平的会议论文时，就发现我可能不适合纯搞学术了，就开始想往工业界转，后面就开始学习agent开发，产品开发过程中也在构建垂直领域benchmark和规划后面垂直模型后训练@Xudong07452910 所以我现在和我的supervisor说我打算投会议而非journal，新点子迭代快，而且可以马上看到结果和产出，但是明面上学术委员会仍然看不起会议，认为没有含金量@LDeng84532 即使在湾区的Stanford，能不被找工作折磨一番的，也是少数@uncle_moon_x 只要你做RA，你的研究课题就被限制死在老板获得funding的课题框架内 @snowboat84 4/4 真正有价值的应对方式在于主动定义值得长期投入的问题、与导师协商个性化路径，并依赖自驱力而非体系保证 - Be able to define a GOOD problem is more critical now. Working on such problems usually makes you away from what is popular and convenient now. It is never an easy thing to work for future rather than right now. But that is what we believe and make us live as a true expert that can’t be replaced quickly @lasiafly 好好和你导师唠唠，一起设计个你们真心认可的PhD program，你的committee大概率也不是木头人，没准有人和你想的一样，根据自己的目标去调整你的产出，满足了毕业的基本条件之后，不一定需要接着重复满足这个评价体系，而是可以试试做你觉得重要的事，然后主动去找会认可你的人@areuuhungry 可能无论在哪里，做一些自己热爱并且能够帮助社会的事情才是有意义的，不过虽然能注意到这一点，自己还是没有能力与勇气做出改变，只能随波逐流@liiiaff 碰见贵人和好导师的几率跟中六合彩差不多，现实的情况是世事变化，导师们其实也脑壳很疼，不知道未来在哪里，一切都要靠自己，都是悟出来的，需要自驱力，不要傻等@yuyy614893671 最近跟几个在国内985大学当老师的朋友聊，他们都说现在真的不太情愿手把手指导研究生干活了，用AI做科研省事又省心，教研究生教半天一堆问题，有的时候还有各种情绪，想想何必呢@xiaohuiyuan

They actually did the meme

Gemini 4 Carbon is our next frontier model. It’s state-of-the-art across cybersecurity and long-horizon capabilities.

🚨Agentic Multimodal ICL: Can an MLLM learn to build its own in-context examples: what to look for, what to keep, and when it has enough? We post-train MLLM agents to do this with tailored tools. ActICL beats similarity retrieval by +11.6 points, and boosts frontier models including GPT 5.6! Key takeaways: 1️⃣ We cast ICL retrieval/context construction as an agentic task: the agent searches a demonstration bank with retrieval goals it writes itself, reads promising candidates, curates its context, and commits it, all under token budgets. 2️⃣ We train ActICL agents with rejection-sampling SFT followed by joint RL over acquisition and answering. Across 7 benchmarks and 3 backbones, ActICL beats the strongest trained baseline by +4.4 points while using only ~3.5 demonstrations on average. 3️⃣ ActICL contexts transfer: a 9B ActICL agent builds contexts that raise GPT-5.6-Sol by +3.5 points over standard retrieval, and it beats a Gemini3.5-Flash-powered agentic workflow on all 9 model–benchmark pairs we test. 👇🧵

Hi, Tomorrow we are re-opening the Pro $200 subscriptions to new subscribers, but together with it we are also changing how we calculate the usage for it. In effect, if you do the math, it will net out at half the dollar in API spend compared to the old Pro $200 plan. Now that it's said, let me explain why this is happening and why you will still get more work done than if you were on the Pro $200 subscription one month ago. (a) We didn't want to compromise in other ways and are committing to not reintroducing the 5h limit, so that you can fully use the weekly usage when you want. (b) On the subscription, we guarantee that over time you always get more work done and with an increasing level of quality. This means that you will continue to get more value per dollar spent as a result of models getting more efficient and us passing down the improvements in the form of API price reductions. (c) We don't want to put an incentive on ourselves to artificially inflate the API list prices to make it look like you are getting a lot (and workaround it through discounts, etc). Instead we want to continue to both rapidly reduce prices and increase capabilities of models on the API. This week we introduced GPT-6 Sol and GPT-6 Luna at 50% of their previous price. Over time, we see prices go low enough that it makes sense for most to buy usage as needed without there being a significant gap between what you get in a subscription and what you get in the API for a dollar spent. (d) Tomorrow, we are adding more things to the subscription that won't draw on the usage, I won't reveal what that is yet. I wanted to be transparent before all the big announcements tomorrow. Lots of new exciting things are coming to the subscriptions that will make it super compelling, but I wanted to make sure to share this change ahead of time so you can all understand it before we shower you with good news. Codexingly, Tibo

Or… what if we gave you $100 in Codex credits if you tell us what you love about GPT-5.6 Sol or why you switched? Tweet it, claim your gift, enjoy more usage. First 10k get the free tokens! switch-to-codex.openai.chatg…