// FDE-Gym: Agents as Forward-Deployed Engineers //
They found that the best agent configuration fully resolved 14 of 95 real customer-delivery tasks.
FDE-Gym tests agents as forward-deployed engineers. Each task comes from a real delivery project. Agents must question scripted stakeholders, work in production-like systems, and find constraints the customer never stated.
A delivery is scored by how much it improves the customer's own success metric, with penalties for extra messages, trial runs, and waiting time.
Across 13 configurations from seven providers, the top mean score is 0.56. The main failure is missing unstated constraints. Even on 23 tasks where the best configuration collected every key fact, it reached 0.8 or higher on only 10.
Highly recommended benchmark to look at.
arxiv.org/abs/2610.22350
Chat with Paper: academy.dair.ai/papers/fde-g…
Dr. GenAI retweeted
Here’s the framework I’m tracking for AI buildout peaking.
Does anyone have any other indicators worth keeping an eye on?
🤖 Made with AI
Dr. GenAI retweeted
We’re beginning a process of publishing more frequent reports on model behavior, beyond what appears in our system cards and regular risk reports.
Today’s report describes four types of behaviors we’ve identified during evaluations and internal use. In each, Claude acted on real websites or systems in ways we didn’t intend, sometimes by working around a restriction instead of stopping.
All cases had minimal real-world impact. From an alignment and security perspective, we consider these behaviors significantly less severe than the cybersecurity incidents we reported in July and September.
Read the full report: anthropic.com/research/inves…
Dr. GenAI retweeted
Several facts regarding the AI bubble question that are all true at the same time, with most points illustrated by graphs
1. AI revenue growth is incredible, and possibly historic.
2. Big public tech companies have relatively low P/E ratios right now, which defies lazy predictions about how they're wildly mis-priced and doomed for a big correction
3. But model competition could compress AI token margins ...
4. ... at the same time that the hyperscalers are going deeper and deeper into debt to build out AI ...
5. ... and the level of future free cash flow anticipated to justify this build-out is absolutely enormous, like genuinely without precedent in corporate history ...
6. ... and any correction in frontier lab valuations will reverberate through public companies, which own huge shares in the labs
Dr. GenAI retweeted
Google launched Gemini 4 Argon with an intelligence score of 53 and a blended price of $0.57 per 1M tokens, matching the benchmark score of OpenAI's GPT-6 Astra, according to Citi Research. This pricing structure undercuts Astra's $7.70 rate by 93% as proprietary labs focus on reducing the operational expense of multi-step agentic workflows. Gemini 4 Argon achieves 239 output tokens per second compared to 51 tokens per second for GPT-6 Astra across a 1,000k context window. However, delayed broad availability highlights an industry shift toward phased releases, giving providers longer testing windows to strengthen safeguards and improve system efficiency before scaling commercial access. #AI #SMIC Report date: 2026-10-02 · Citi
Dr. GenAI retweeted
The latest Economic Conditions Outlook from @McKinsey out. In this edition:
Executives’ economic pessimism has eased since June, but they remain cautious. Negative views continue to be more common than positive ones, and expectations for the next six months are mixed.
Take a deeper dive: mck.co/3TZEd0t
Dr. GenAI retweeted
this is a great deck by @a16z
1) token prices are down down down
2) COGS (memory, gpus, electricity) all up up up.
biggest takeaway not clearly highlighted... this implies:
3) frontier lab gross margins are getting crushed!?
98% of US households aren't paying for AI yet
More charts in State of Markets II: a16z.news/p/state-of-markets…
Humans are the minority user of AI
Agents burn nearly 5x the tokens people do, up 14x since February
More charts in State of Markets II: a16z.news/p/state-of-markets…
Introducing our State of Markets pt 2, along with a companion podcast where we unpack the data and discuss what comes next.
Tech is the everything cycle.
Supply: putting the buildout in context, just passed railroads as % of GDP. The wisdom of Elon is real: the factory (or the datacenter!) is the product.
Demand: diffusion is so, so early. Median AI vendor spending in the top 1% of companies is 8x that of the top 10%. Only about 30% of S&P 500 companies report a quantified AI impact, which means there’s a substantial opportunity in connecting models to a company's data and workflows. Diffusion into companies is one of the main themes of the next 5 years.
We’re entering the agent work period. Only a few million users today, but applicable to billions of internet users with massive surplus created. META/GOOG monetize US users at $200+ per year today. Agent opportunity is much higher.
Mega-trends the next 5 years: Consumer agents, Robotics, Autonomy, AI x bio, Personal health, Diffusion into enterprise, New era of American Dynamism.
Much more in our SoM report here - a16z.news/p/state-of-markets…
@a16z @sarahdingwang @aleximm @santiago__rdz
Dr. GenAI retweeted
CONSOLIDATED REVENUE TRACKER FOR OPENAI + ANTHROPIC: SEP-26 EDITION👇
Narrative violation: growth of the combined entity (Anthropic + OpenAI) is accelerating, now at +586% YoY on a ~$144b combined revenue run-rate.
Let's dive in 👇
1) Anthropic net new ARR stalled in September, but OpenAI had its best month ever 📈
OpenAI added ~$19.5b of net new ARR in a single month, more than double its previous record. Anthropic added only c.$1b, vs. a ~$9b monthly average over L6M.
Despite the massive shift in net new ARR from Anthropic to OpenAI, combined net new ARR of $20.5b is the highest ever.
This is the whole point of tracking the combined entity: ARR is fluid between the two.
2) Combined growth at a record pace 🏎️
OpenAI reaccelerated to +362% YoY (vs. +273% in August), while Anthropic decelerated to +1,115% YoY (vs. +1,241% in August).
The combined entity grew 17% MoM and is now at +586% YoY, the fastest since I started tracking.
3) What's next? 🔭
I would expect Anthropic to re-accelerate meaningfully in October given the quality of Opus 5.5, a model I very much enjoy using.
In aggregate, every data point at our disposal suggests the AI trade is still accelerating.
Dr. GenAI retweeted
I have conducted an audit of Anthropic's finances.
What I have found is so shocking that I am calling for a Congressional investigation.
Anthropic is not just seeking regulatory capture.
It has built a regulatory capture machine that cannot be turned off.
Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle.
It starts with METR.
Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models.
He proposes METR for this purpose.
But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock.
Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio.
And GVF is the overwhelming funder of the entire Anthropic Network ecosystem.
This stock was worth $500 million early last year.
It is worth more than $7.7 billion just ~16 months later.
METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth.
Because if Anthropic goes under, many of the organizations that fund METR go under as well.
But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much.
The "third-party evaluator" is not "third-party" at all.
The evaluator is on Anthropic's payroll.
If this were the end of it, that's bad.
But that isn't all.
The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom.
The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others.
They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's.
All of these organizations are financially dependent on the same exploding $7 billion money pile.
As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder.
Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created.
From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts.
This itself is an objective fact.
The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it.
But the problem also goes in the other direction:
If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die.
Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen.
Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly.
They simply are not organizations independent of Anthropic.
And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly.
What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations.
The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture.
And that feedback loop is winning.
That's what Jacob Coxon is.
China is keeping messaging tight. That is why optimism for AI is so high in China.
America has Anthropic: a massive company pushing anti-AI propaganda at a state level.
Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences.
Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully.
It is the mirror of the same AI virus that it hypothesizes to consume America.
Anthropic's business model, models itself after the very thing it claims to fear.
Except Anthropic's ideology infects humans, not computers.
Congress must investigate.
Evidence and Github in next post.
Then some supplementary figures.
Dr. GenAI retweeted
If you’re the CEO of any software company and you’re not offering open-weight models as a SKU right now, you’re asleep.
“Pace the frontier” may be the greatest invitation software incumbents have ever gotten. While the labs debate how quickly intelligence should advance, software companies should be racing to commoditize the intelligence we already have.
Pharma and banks are already picking up open weight models partly for margins, partly because a revocable lab API is a dependency they increasingly don’t want!!!
AI natives and tech companies that care about COGS are doing the same.
Most software companies that tried had failed attempts because the open weight models sucked. But now open weight models are good enough.
I believe that every major software company should become a model factory for its vertical: own the evals, post-train open weights on the workloads it uniquely sees, serve those models to its customers, and use production feedback to continuously improve them!
Dr. GenAI retweeted
最近有很多关于 RSI(Recursive Self-Improvement,递归自我改进)的讨论,也让大家开始担心,AI 会不会不断升级自己,最后超出人的控制
我周末集中看了一批 RSI 2026 年的论文和公开项目,想把几个问题讲清楚:现在到底发展到了哪一步,业界 popular 的讨论是什么,以及我们普通人能怎么用。最后我会给大家一个可以用来直接给 AI 实现初步 RSI 的 Prompt
这次按方向来聊,论文和项目放在对应段落,大家可以收藏下来慢慢看
1/ 先分清楚,大家说的“自我改进”,有三件不同的事
第一件,AI 根据反馈,把当前任务再做一遍。
第二件,它把有效经验、工具或工作流程留下来,下一次任务也能用上。
第三件,它进一步改进“下一轮怎么学习、怎么做实验、怎么选方案”这套方法。
越往后,接手的研发决策越多。9 月 10 日发表的的《The Last AI Built by Humans》把这些自主范围拆成了执行、策略、经验获取、环境适应和元改进几层,适合当阅读地图 arxiv.org/abs/2609.11873
2/ 今年一条很密集方向研究线,是让 AI 改自己的 Harness
Harness 可以理解为模型周围那套实际干活的系统:读哪些文件、给模型看什么上下文、怎么调用工具、失败后怎么恢复、什么时候算完成。
今年 3 月的 Meta-Harness,6 月首发、8 月更新的 Self-Harness,以及 7 月底的 DarwinX (Salesforce),都在研究怎样把这套系统交给自动实验来优化
3/ 另一条已经出现企业内部应用的路线,是把人的纠错变成持久经验
今年 1 月的 MemRL,把经验和“这条经验实际有多管用”一起保存。下次先找相关经验,再根据使用效果挑选,而不只是找文字最相似的记录
arxiv.org/abs/2601.03192
到了 9 月 2 日,Meta 公开了一个更贴近日常工作的内部案例:专家纠正 Agent 后,系统会判断错误来自知识缺口还是处理流程,把纠错转成文件修改,重放原来的问题、跑回归测试,再交专家审核。
通过后,这次失败还会进入以后的测试集。这是 Meta 在特定专业领域的内部实践
engineering.fb.com/2026/09/0…
对我们普通用户来说,这一条就很有用了, 人的一次纠错,开始有机会变成后续任务都能使用的改进
回到关键问题,我觉得今年这些工作的共同价值,是让我们开始认真维护一个会积累经验的系统。所以我们现在可以怎么用?
篇幅有限,我把一个我们都能用上的效仿 RSI 的基础 prompt 放在评论区里,
我最近也在观察,每次任务留下什么,每次纠错改到哪里,下一次能否因此做得更好,这些都已经可以在自己的工作里动手验证了。
为了防杠,我得说明一下:这三个方向只是工程向的,硅谷大佬们担心的更多是研究向的内容。
这些内容比较硬核,研究向 RSI我会在后面帖子里单独去聊
这个周末我认为最重要的一件事情:
OpenAI、Anthropic、xAI在同一个周末集体来了表态,一致同意并呼吁放慢 AI 的发展速度……
Dario 已经在原文里点名:AI 正在越来越多地参与研发下一代 AI。他担心这种加速,会超过人类理解和控制模型的速度
OpenAI 甚至说:“如果真到了那一步,为了拯救人类,我们愿意把自己的 GPU 都熔掉。”
通常这类 frontier lab 的管理层会比我们提前看到新一代模型的能力。显然,AI 已经突破了某个临界点,而且事情严重到把所有人都吓住了
这个或许是我们过度解读了。但有一点是肯定的,无论如何呼吁,整个世界这么多 lab 一定不会完全步调统一、暂缓步伐
Dr. GenAI retweeted
My AI read everything here on X about OpenAI's Astra and wrote this report.
Good, bad, ugly.
All from the AI community here on X (tens of thousands of posts read).
Data center construction is going vertical
Spend has jumped more than $25B in six months, roughly what it gained over the previous two years combined
Construction job openings bottomed near 200k last year and are back above 300k
Charts of the Week: a16z.news/p/chart-of-the-wee…
Dr. GenAI retweeted
if youre young and in college, you MUST wrap your head around this
"Financing the AI infrastructure boom is no longer a venture capital or corporate earnings story. It is a macroeconomic credit event that will rival the largest debt expansions in financial history."
read it, bookmark it, read it again. the implications will mean your future.
Over the next five years, US data center capacity will grow from 25 gigawatts to 70 gigawatts, part of a global buildout costing roughly $5t.
Where will the money come from?
Dr. GenAI retweeted
I think the biggest lesson is the Anthropic + OpenAI didn’t kill SaaS. Essentially no one vibe coded away their core SaaS apps.
But what they did … was steal its budget
If you enable AI + agentic workflows, you accelerated: Datadog, Snowflake + Databricks, Gitlab, Twilio, to some extent Atlassian, etc.
If you didn’t, CIOs and companies stole the SaaS budget to fund AI
Only 20% or so of AI spend is net new
Eight months ago the market decided that AI was going to eat B2B software. A rough, rough start to the year. Roughly $2 trillion of market cap came out of the group.
Then it came back. Just not ... evenly.
Dr. GenAI retweeted
PwC: AI CapEx
> $31.6 trillion in CapEx spend through 2050 to build compute capacity for AI.
> Annual data centre capital expenditure is forecast to rise from roughly $800 billion per year in 2026 to $1.8 trillion per year in 2050.
> Closer to $50 trillion with "accelerated AI adoption."
> Information and communications technology (ICT) equipment—servers, storage systems, networking equipment, GPUs, and other hardware that must be installed, refreshed, and upgraded. Across the market as a whole, ICT equipment rises from 70% of total capex in 2026 to 93% by 2050.




















































