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奥特曼:PC上装内存太浪费,最好留给AI数据中心

华尔街见闻OpenAI CEO奥特曼语出惊人:本地运行AI极度浪费,全球内存应集中云端,他预警AI将引爆史无前例的“漏洞潮”,底层架构亟待重构;更透露“AI自我复制建厂”已在掌握。比起万亿IQ单脑,他更期待万亿AI智能体共建社会,全面颠覆未来。
· AI应用,OpenAI,Agent智能体,基础设施
AI 资讯

DeepSeek发布全新Agent架构

华尔街见闻
· 大模型,AI应用,DeepSeek,Agent智能体,模型发布

DeepSeek公布下一代agent harness理念

华尔街见闻告别被动响应,Agent正加速进化为全天候“数字同事”。DeepSeek公布下一代agent harness理念,凭双层架构、事件驱动与存算分离强势破局多任务并发瓶颈,实现跨会话自主推进。彻底解放人类注意力,领跑商业与人机协作新范式。
· 大模型,AI应用,DeepSeek,Agent智能体

已进入第二版!傅里叶帮Meta研发机器人原型,打造“Muse物理化身”

华尔街见闻Meta与中国机器人公司傅里叶联合开发人形机器人原型“Max”,并启动第二代研发,计划将消费级AI智能体Muse延伸至家庭场景。新原型将升级操控与感知能力,Meta同步在上海、深圳招募量产人才,但目前暂无销售计划,消费级人形机器人布局仍处研发阶段。
· AI应用,具身智能,Meta,Agent智能体,招聘HR,产品更新
AI 资讯

DeepSeek又开源了!公布Super agent架构

网易科技
2026-11-01T00:30:27+08:00 · 大模型,AI应用,开源,DeepSeek,Agent智能体

I built a scikit-learn for RSI [P]

Reddit r/MachineLearning
I built a scikit-learn for RSI [P]

After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search:

  1. Sampling: How do we find promising changes efficiently?
  2. Evaluation: How do we know a new variant actually improves the agent?
  3. Diversity: How do we avoid converging too early?

That motivated us to build Gear, an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management.

Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI, making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluation.

With GPT 5.6 Luna held at medium reasoning effort, we observed these task pass rates:

Benchmark Base DSH Gear-optimized DSH
AutomationBench Marketing 27.0% 40.0%
AutomationBench Support 17.0% 25.0%
Terminal-Bench 2.1 52.8% 62.9%

At max effort on Marketing, Luna with the optimized harness reached 88.88% objective completion, versus 84.08% for Astra + Codex. Strict task pass rates were 53% versus 57%, so the advantage was in partial completion.

Separately, with both models using the same optimized DSH at max effort, Luna’s estimated API cost averaged $0.0568 per task versus $1.1858 for Astra—95.21% lower, excluding optimization and infrastructure costs.

We’d welcome feedback on candidate evaluation and diversity preservation under limited budgets.

Code · Paper

submitted by /u/Pale-Training-6944
[link] [comments]
2026-10-31 16:33:26 · 大模型,AI应用,开源,Meta,代码生成,Agent智能体,推理思考,搜索RAG,扩散模型,强化学习,模型评测,提示工程,招聘HR,开发者生态
AI 资讯

telemetry.dev

Product Hunt

LLM observability for AI agents

Discussion | Link

2026-10-29 00:13:46 · 大模型,AI应用,Agent智能体,招聘HR
AI 资讯

SpaceO

Product Hunt

Give AI agents their own screen on your Mac

Discussion | Link

2026-10-04 08:20:17 · AI应用,Agent智能体,招聘HR
AI 资讯

Backenly

Product Hunt

The autonomous backend for AI coding agents

Discussion | Link

2026-10-29 21:07:32 · AI应用,Agent智能体,招聘HR
AI 资讯

GitHub Copilot Fusion

Product Hunt

GitHub's AI coding agent that picks and combines models

Discussion | Link

2026-10-29 05:39:35 · AI应用,开源,Microsoft,代码生成,Agent智能体,招聘HR
AI 资讯

Pluto Door

Product Hunt

SSH client with an AI agent and a desktop for every server

Discussion | Link

2026-10-30 22:10:44 · AI应用,Agent智能体,招聘HR,榜单评测

Surviving "The Graduate"

Hacker NewsComments
2026-10-31T10:00:00.000Z · AI应用,Google,快手,文生视频,Agent智能体,搜索RAG,Transformer,扩散模型,强化学习,招聘HR,榜单评测,开发者生态

HackerTube: YouTube built from videos recommended by HN

Hacker NewsComments
· 大模型,AI应用,开源,Google,Anthropic,Meta,代码生成,Agent智能体,扩散模型,招聘HR,榜单评测,开发者生态

EqualAI CEO: Your AI assistant is acting in your name. Who signed off?

Fortune

This summer, investor Katie Jacobs Stanton said her AI assistant, Instinct, sent an email on her behalf without consulting her first, according to TechCrunch. She described the email as innocuous. The breach of trust was not.

Next time, it could be the email that costs someone a job, a marriage or a lawsuit.

The appeal of personal AI agents is compelling. An assistant that can identify priority emails and draft responses, remember your mother-in-law’s birthday and buy a present based on your correspondence is enticing. For people stretched by demanding jobs, caregiving, or a disability, that help can be transformative. But Stanton’s experience raises an important question: when we give an assistant access to our lives, how do we retain control? Assistants such as Instinct and Meta’s Muse act for us rather than simply answering questions or making recommendations. A mistaken purchase may be refundable. An intimate disclosure cannot be taken back.

The harms are already emerging. Forbes reported that when investor Jason Yeh asked Instinct to find open dinner reservations, it booked a table instead, leaving him with a $200 cancellation fee he never approved. TechCrunch reported that one tester couldn’t get his data deleted, and another received an inbox summary hours after disconnecting her account. And in September, security researcher Patrick Wardle exposed a flaw in Muse’s Mac app that could let malware already on a computer hijack the assistant, which Meta quickly patched.

The problem now reaches our bank accounts, too. Last week, major financial institutions reported that personal AI agents were operating inside online accounts. In one case, an agent called a bank without authorization to file a complaint. JPMorgan Chase CEO Jamie Dimon said the bank would not allow unvetted “outside agents to come in inside the company”, and Schwab’s CEO said they’re actively blocking unauthorized agents. White House adviser David Sacks countered that data was shared willingly and these are “in no way cyberattacks.” Each side has a point, which is precisely the difficulty. We have not yet settled on what authorization means when the party acting is software.

The failures are also hard to see. A public record launched this week by the security firm Manifold counts 59 incidents since July 2025, forty-two of them disclosed in the past four months alone. The median incident took 70 days to come to light, and only 11 of the 59 were detected by a security control.

At EqualAI, we have long maintained that good AI governance is good business because trust will ultimately determine whether people adopt these tools at all. We recently convened senior executives who develop and deploy agents to work through a simulated crisis. The exercise exposed the same gaps repeatedly. In our mock company, without sufficient AI governance, audit trails were too thin to reconstruct what an agent had done. Vendor contracts were silent on who answers when an agent errs. Responsibility for oversight was assumed rather than assigned. These gaps were most acute where speed had outrun precaution. Those gaps shaped the roadmap that came out of the exercise.

The legal landscape is moving in the same direction. California law now prevents companies from avoiding civil liability by arguing that the AI acted autonomously. Senators Hawley and Murphy proposed extending federal anti-hacking law to developers and operators whose agents recklessly cause damage.

Those same principles should apply to the assistants people are bringing into their homes. For consumers, they translate into four commitments.

Make permission specific. An assistant should receive only the access a task requires. Permission to read your inbox should not automatically become permission to send emails in your name. Before an assistant spends money, changes who can access an account or shares sensitive data, there should be safeguards in place, which are known to users.

Assign responsibility in advance. If an assistant wrongly cancels your flight, the remedy must be clear. Companies should determine in advance of deployment who investigates an assistant’s unauthorized actions, who can halt them and how users are made whole.

Test with users’ perspective. Independent testing should confirm that assistants ask before acting, respect their limits and fully disconnect when access is revoked. Because models change constantly, testing must be continuous. Wardle’s discovery was valuable. Work like his should be the norm.

Report and study failures. Aviation became one of the safest forms of travel in part because pilots and airlines report near misses and investigators learn from them. Companies should be expected to identify an “AI incident”, preserve the records, notify those affected and correct the underlying cause, while policymakers can establish reporting requirements for serious cases.

Consumers also have a role. They need to understand permissions they’ve granted and how to revoke its access. But the greater responsibility rests with companies selling the tools to define their limits and set reasonable expectations. We shouldn’t need a cybersecurity background to use these tools safely.

We board planes without understanding how they fly because industry and regulators answer for our safety. We need a similar framework for AI assistants. If companies want our inboxes, accounts and errands, they must earn our trust by clarifying what we’re authorizing, how to regain control, and answer for mistakes their assistants make in our name.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

This story was originally featured on Fortune.com

2026-10-31 12:00:00 · AI应用,Meta,Agent智能体,搜索RAG,扩散模型,招聘HR,版权诉讼

Mark Zuckerberg’s $170 million Miami mansion is just one piece of his sprawling real estate empire that includes a 196-year-old castle

Fortune

With a net worth of $222 billion, Mark Zuckerberg is one of the richest people in the world and the owner of a massive real-estate portfolio that spans the entire U.S.

His latest purchase from August is a 440-acre estate and accompanying 196-year-old castle in County Waterford, Ireland, a purchase that reportedly set him back 51.17 million euros, or about $57.1 million.

Yet, his priciest purchases dates back to earlier this year, when the Meta CEO and his wife, Dr. Priscilla Chan, reportedly purchased a luxury mansion on Indian Creek Island in Florida near the in-progress home of Amazon founder Jeff Bezos.

The 300-acre, man-made Indian Creek Island is near Miami and Miami Beach and contains a mere 41 lots with approximately 84 residents, the Wall Street Journal reported. Home prices on the island start at about $60 million, and records show the billionaire paid $170 million for his waterfront home with nine bedrooms and 11 full bathrooms, the Miami Herald reported. Miami-Dade County property records note the owner as a land trust.

Even an undeveloped property on the exclusive island can fetch big bucks: One vacant lot roughly the same size as Zuckerberg’s sold for a reported $105 million in 2025.

The property Zuckerberg reportedly purchased at 2 Indian Creek Island Road puts him in the most coveted area of the already exclusive island, said Mitchell Duchon, a Miami Beach–based real estate agent with the Corcoran Group who specializes in high-end waterfront properties. On the western side of the island, where Zuckerberg’s property is reportedly located, lots are about 80,000 square feet, compared with about 50,000 square feet on the east side, according to Duchon. On this side, where Amazon founder and fellow billionaire Jeff Bezos also owns two lots, residents have better access to the open water of Biscayne Bay and a more expansive view with great sunsets, he added.  

“That side of the island is perceived to be the most appealing,” Duchon said.

Bezos has been buying up properties on Indian Creek Island since he announced in 2023 he was leaving Seattle for Florida. The Amazon founder first purchased a $68 million home that would end up being just a few doors down from Zuckerberg’s in 2023. Near the end of that year, he paid another $79 million for a neighboring property with the intention of combining the lots into a single compound—a trend Duchon said is increasingly common among wealthy buyers because of the scarcity of truly expansive waterfront properties in the area. Meanwhile, Bezos is reportedly living in a third Mediterranean-style property also on Indian Creek Island, on the east side, which he snapped up for $87 million in 2024. Last year, Bezos sold one of his Seattle homes overlooking Lake Washington for a record $63 million, the Puget Sound Business Journal reported.

Indian Creek Island operates as an independent municipality with its own government and private police who patrol by air, water, and sky. Access to the island is controlled by a single gated bridge, making safety and privacy a defining feature. Most of the island’s interior comprises an 18-hole golf course and the Indian Creek Country Club, with a limited number of members owing, in part, to a difficult admissions process and a $500,000 initiation fee.

Zuckerberg is one of the latest billionaires to pick up a Florida property, especially in light of a proposed ballot initiative to be voted on in California next week that would impose a one-time 5% “billionaires tax” for any individual worth at least $1 billion dollars retroactive to Jan. 1, 2026. (To be sure, Zuckerberg still very much calls California home, having invested $50 million through Meta for a Sacramento downtown revitalization and AI-focused project earlier this year.) Google cofounder Larry Page also snagged a $71.9 million home in Miami’s Coconut Grove neighborhood in January that he plans to combine with another property to form a total $173 million waterfront compound.

Indian Creek Island, in particular, is home to a number of high-profile names, who, besides Bezos, include the financier Carl Icahn and former NFL quarterback Tom Brady. 

Zuckerberg’s reported newest property is just the latest addition to the tech CEO’s growing portfolio of luxury homes across the U.S.

Palo Alto compound

Zuckerberg’s home base remains Palo Alto, where, according to the New York Times, he owns 11 properties in the Crescent Park neighborhood. Over more than a decade, Zuckerberg has poured $110 million into buying adjacent properties, in some cases drawing complaints from his neighbors. 

Vegetation covers the front of Meta CEO Mark Zuckerberg’s house in Palo Alto, July 2012.
Noah Berger—Bloomberg/Getty Images

Hawaiian estate

On the island of Kauai, about 100 miles northwest of Honolulu, Zuckerberg owns a $300 million property commonly known as Koʻolau Ranch, spanning roughly 1,400 acres. He quietly added about 1,000 additional acres to the compound last year, Architectural Digest reported, bringing his total Kauai holdings to more than 2,300 acres. 

The ranch is one of Zuckerberg’s most secretive properties. Alm

2026-10-31 15:07:48 · AI应用,具身智能,Google,Meta,Agent智能体,搜索RAG,扩散模型,招聘HR

Tech stocks just lost more than $1 trillion in a day—a gothic horror test for the ‘moats’ Warren Buffett taught Wall Street to love

Fortune

Markets are ushering in a particularly gothic Halloween as the AI trade exposes a crumbling aristocratic pile of early 21st century capitalism. Wall Street has been describing itself in castle-adjacent terms for years.

Jamie Dimon has long talked to JPMorgan shareholders about the bank’s “fortress balance sheet.” His 2015 letter set aside a section to answer the question of what the phrase even meant. By 2021, he’d added a second castle word, warning that “the moats that protect this company are not particularly deep.”

The moat came from Warren Buffett. In a 1999 Fortune article with Carol Loomis, he argued that the key to investing is a company’s competitive advantage and how durable it is, and that the businesses worth owning have wide moats around them.

The gothic novel’s favorite picture of danger is a castle falling apart, and for about 250 years the two have traded images: the castle and the moat, the vampire, the creature built in a lab. This Halloween season has them rushing to the front of the conversation, with a two-day rout in tech stocks on Thursday and Friday turning on whether the AI giants’ moats will hold, as gothic horror booms on bookshelves and movie screens.

Horror took 15.5% of the domestic box office in 2025, according to The Numbers, and U.S. horror book sales rose 7% through the first nine months of 2025, per Circana BookScan, while adult fiction overall grew just 1%. The New York Times asked Friday why every book is gothic now, with publishers putting the label on everything from family sagas to space operas, and several film critics commenting that Nathan Fielder’s smash-hit business-scandal documentaryYou Can See Everything is a twisted, even gothic romance in the tradition of Alfred Hitchcock.

The moats get tested

The selling started with Alphabet. The stock fell 12.6% Thursday, wiping out roughly half a trillion dollars in market value, after the Google parent’s quarterly report. Revenue from the core search unit fell short, partly because of the popularity of less-profitable “AI mode” searches and the company burning cash faster than expected. Its free cash flow came in at negative $9.1 billion, against the negative $2.23 billion analysts expected, per FactSet.

The S&P 500’s worst performer was software giant ServiceNow, which dropped 27% the same day on slowing revenue growth and disappointing guidance. By Friday morning, it was Amazon’s and Intel’s turn, with Intel down roughly 25% before the opening bell. Big Tech lost nearly $900 billion in market value after another sharp rise in AI spending in the third quarter, the Financial Times calculated. Nasdaq 100 companies lost more than $1 trillion in market value Friday alone. Reddit, whose shares fell more than 22% premarket Friday, is pulling up the drawbridge: it’s moving to a logged-in experience as AI eats search.

The irony is spooking investors. AI is eating into the businesses that throw off the cash, and the cash is going into AI anyway. The hyperscalers have raised $326 billion in stock and bonds this year to pay for the build-out, more than triple 2025, according to S&P Global Market Intelligence data.

The question underneath it is the one Apollo chief economist Torsten Slok put to clients the morning Alphabet fell. “With AI disruption potentially around the corner, there is a lot of discussion about moats at the moment,” he wrote in his Daily Spark note Thursday.

He built on finance professor Aswath Damodaran, the “dean of valuation,” who compiled a list of sources of competitive advantage, including brand strength, switching costs, network effects, cost advantages and legal protection. Then he boiled it down to an AI moat checklist of three questions: does the company own data its rivals can’t get, is its product part of how customers do their jobs every day, and can it charge more without customers walking away?

Judging by that third test, the companies selling AI don’t seem to measure up. OpenAI’s new GPT-6.1 Terra model costs up to half as much per token as the version before it. An index from expense-management firm Ramp, which looked at what companies actually pay Anthropic and OpenAI, shows token prices down roughly 35% since the end of June, in what Axios called a price war.

The companies paying for AI aren’t getting much back yet, either. In an Oct. 24 note, Slok sent a tremor through Wall Street terminals with an argument that AI “has delivered zero financial payoff to corporate America outside of tech.” Revenue per worker and profit margins are rising for the Magnificent Seven and flat for the rest of the S&P 500, he noted, and mainstream companies are starting to treat token spending as a cost center that weighs on margins rather than a source of growth.

The first castle was a fake

Xavier Aldana Reyes, a researcher at the Manchester Centre for Gothic Studies, told the Times that interest in the gothic tends to surge during periods of social anxiety and political turmoil. Literary agent Mary C. Moore put the genre’s subject more bluntly, telling the paper that “Gothics are all about exposing what is rotten at the core of something shiny/wealthy.”

The genre was invented by a scion of wealth who knew the old order from inside and drew a salary without working at all. Horace Walpole was the son of UK Prime Minister Robert Walpole, and hisThe Castle of Otranto, published in 1764, is generally regarded as the first gothic novel. 

He passed it off as a translation of an obscure Italian work, found in the library of an old Catholic family in northern Eng

2026-10-31 11:30:00 · 大模型,AI应用,OpenAI,Google,Anthropic,Microsoft,Agent智能体,搜索RAG,扩散模型,强化学习,招聘HR,网络安全,财报,榜单评测,开发者生态

AI boom hits a wall on Wall Street as OpenAI cuts fundraising target while Meta gets a credit warning over its cash flow, spending and monetization

Fortune

The seemingly endless gusher of capital that’s been showered on Silicon Valley during the AI boom may be coming to an end soon as Wall Street pushes back on massive spending plans.

Following a brutal selloff in tech stocks over the past week, OpenAI slashed the target on its latest fundraising round to $20 billion from $30 billion, sources told the Financial Times. That also cut its expected valuation to $1.25 trillion from $1.4 trillion.

The funding round was also upstaged by Anthropic, according to the report, as OpenAI’s arch-rival prepares to go public next month. Ahead of its IPO, Anthropic filed an S-1 on Monday that showed revenue has soared and its cash flow turned positive in the first half of this year.

OpenAI didn’t immediately respond to a request for comment.

The timing couldn’t have been worse. Quarterly earnings reports from hyperscalers this week spooked investors. Alphabet saw its cash flow sink deeper into negative territory in the third quarter, with the Google parent also joining Meta and Amazon in charging ahead with massive spending plans. On Thursday, Alphabet fell about 12%, its worst day on record.

To be sure, there were other positive signs as well, in addition to Anthropic’s bullish S-1. Microsoft reported strong cloud growth and stayed cash flow positive, though it said its capital spending will accelerate, while AI chip maker SK Hynix blew away earnings forecasts.

But the narrative had already turned bearish, especially after AI debt fears sent shares and bonds of “neocloud” companies tumbling earlier this month.

At the same time, OpenAI was seeking more capital just as other AI giants tapped financial markets heavily. This past week alone, Elon Musk said SpaceX closed $40 billion in debt financing, Meta announced plans to raise $95 billion in equity, and Microsoft raised $25 billion in a bond sale.

The prospect of hyperscalers continuing to hemorrhage funds was too much for S&P Global, which cut its outlook on Meta to “negative” from “stable” late Friday, citing negative cash flow and uncertainty around capital expenditure ​plans.

The credit ratings agency maintained Meta at AA- but warned ⁠the social media leader’s leverage could top 1.0x as soon as 2027, which is the downgrade threshold for the current rating. That’s up from about 0.1x in 2025 and primarily reflects its strategy of leasing data center capacity rather than from its debt issuance.

Meanwhile, S&P sees Meta’s capex reaching $140 billion this year, topping $225 billion in 2027, hitting $252 billion in 2028, and then flattening out from there. As a result, it expects free operating cash flow to remain negative: -$22 billion this year, -$71 billion in 2027, and -$39 billion in 2028.

Of course, other hyperscalers are spending heavily and running cash deficits, but S&P also said Meta’s ability to monetize its AI investments is more limited than that of its peers.

“While we believe the company intends to monetize these investments via improved advertising tools that support faster ad revenue growth, as well as from the development of new revenue streams stemming from its introduction of agentic apps such as Muse, it is unclear when these revenue streams will generate material contributions that exceed their rising costs over the next few years,” S&P said.

Meta’s rivals, however, are monetizing their data centers by leasing compute capacity, including to frontier model labs and enterprise customers, it added.

In a statement to Fortune, a Meta spokesperson said the company disagrees with S&P’s assessment of its AI monetization capabilities, pointing to “significant product and business opportunities” ahead.

“Meta is already delivering stronger engagement, better recommendations, and greater returns for businesses as a result of AI investments,” the spokesperson continued. “We believe the technology and infrastructure we’re building now will open new revenue streams, create long-term shareholder value, and bring us closer to delivering personal superintelligence to everyone.”

The bearish environment on Wall Street also comes as rising bond yields have sent borrowing costs sharply higher, the political backlash against data centers is expected to help Democrats retake Congress, and the Iran war could re-escalate after midterm elections.

Still, some bullish voices aren’t convinced the AI boom is about to go bust, and even think some retrenchment is a good thing.

According to market veteran Ed Yardeni, the AI investment boom faces financial, physical, and political constraints. If any of these constraints worsen, stocks could dive further, especially considering that AI is now a major driver of corporate earnings and GDP growth, he said in a note this week. 

“However, we view these constraints as healthy for the durability of the AI boom!” he added.

For example, if the supply of capital doesn’t flow as easily, it could lead to more efficient allocation, lowering the risk of overinvestment. Yardeni expects hyperscalers to retain access to financing—thanks to their strong balance sheets—and keep spending.

In his view, it all comes down to the U.S. economy, which he said has performed remarkably well over the past few decades. Even the midterm elections are unlikely to derail it, regardless of which party controls Congress.

“Further AI jitters therefore could set up buying opportunities rather than signal the end of the boom,” he said.

This story was originally featured on Fortune.com

2026-10-31 16:41:35 · AI应用,融资,OpenAI,Google,Anthropic,Meta,Microsoft,Agent智能体,搜索RAG,扩散模型,强化学习,招聘HR,网络安全,财报,榜单评测,开发者生态

Regulate the Deployment, Not Just the Model

LessWrong

A who-does-what plan for AI safety, built around Trusted Deployers: certified hosts that keep dangerous models guarded without ending open access.

AI has reached a new level of public interest. That may seem like a silly thing to say, as it’s been of interest for a while. But prior (broadly public) interest was about it as a tool, or about the money to be made or lost, or about an effect on jobs.

The new interest demands policy. It demands action. The new interest is connected with risks I consider real. Being real doesn’t make these risks certain. But being real, even with uncertainty, is reason enough to act.

I’ve written lately about risks, cybersecurity risks, and loss-of-control risks. These tried to explain complex topics, starting from the initial premise. Recommendations follow the explanation. This piece will be a bit different. Its focus is upon what I think we should be doing. You might consider it a policy piece. Policy in a completed form has to be a bit broad. There’s always more than one thing going on. As such, I’ll lay out my outline first, and then offer below the extra detail, background and context that I think is important to understanding each point in that outline.

If you’re scanning the outline and see something you disagree with, I’d suggest you continue on, because the outline is, by design, abbreviated, and not fully descriptive.

This isn’t the only effort of this type. Institute for Progress (IFP) and AI Futures are two well known efforts. What distinguishes this most of all is thinking more deeply about how models are deployed. This is a background element that’s somewhat ignored, because it’s not as novel and is not as visible to users. But it’s absolutely critical. I don’t expect this plan would replace plans from well known organizations, but by illustrating a gap, I hope to inspire improvement amongst those plans.

Who and What

This outline is categorized by who would be expected to do what. In some cases, someone is expected to do it, but there’s also another role, of providing the incentives that improve the expectation they will.

Enforcement is deliberately vague. It depends on jurisdiction, has to fit existing legal frameworks, and will need to be worked out, and negotiated, through the political and legal systems. It would feel like I was putting the cart before the horse to even guess at how that would take shape at national and international levels.

Diagram of four bands: society and governments, governance, model providers, infrastructure. Frontier labs send weights to trusted deployers. Open labs send weights above the threshold through model clearinghouses to trusted deployers, and weights below it to public download. The US government backs an independent regulator and holds talks with the Chinese government. The regulator certifies and funds independent evaluators, who evaluate both kinds of lab and audit trusted deployers. Users get API access through trusted deployers.

Who does what. Model weights above the threshold reach users only through certified trusted deployers. Independent evaluators, certified by a regulator with legal authority, test the models and audit the deployers.

Layer 1: Infrastructure and Distribution

Trusted deployers

Who are they? A new designation that existing and new organizations can be certified as. Expect major cloud providers (AWS, Azure, Google), AI labs (for self-owned) and neoclouds to join immediately. The plumbing largely already exists, but needs standards and certification.

Revenue model? Charging for compute and operational services as a utility function. Or, an internal unit (for a lab, government, or large organization)

Core Responsibilities

  • Develop secure environments to host models that receive an independent AI Deployment Safety Level (AIDSL) rating. Example, but not comprehensive list of requirements:
    • Comprehensive monitoring, telemetry, and auditing (including multi-turn agent pattern detection).
    • Resistant to external cyberattacks and insider threats.
    • Resistant to internal cyberattacks. At minimum red-teamed by deployed model and a second highly capable model.
    • Guardrails applied by default to all deployments which prevent access of dangerous capabilities.
    • Strict controls for any guardrail exceptions as used for “limited” access deployments. For example, accessing users must be known directly, or an evidence chain validated using only trusted organizations.
  • Understand the sensitivity of any model deployed. Never deploy a model in an environment with an AIDSL rating insufficient to the combination of deployment type and model sensitivity.
    • Use internal qualification for small custom models and Independent Evaluator data for larger/frontier models.
    • Models under training assumed to be at least X% more capable than the best model trained to date at the given scale. If the training scale is unprecedented, increase X by a size factor. (X should be established by independent evaluators)
  • Perform or validate Know Your Customer (KYC) verification for all model providers, for Independent Evaluators and anyone with access to deployments with guardrail exceptions.
  • Provide access to Independent Evaluators to confirm secure environments meet standards

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2026-10-31 19:04:29 · AI应用,具身智能,Google,Microsoft,Agent智能体,扩散模型,招聘HR,网络安全,榜单评测,开发者生态
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Explaining “AI might obliterate you” to senior policymakers in India

LessWrong

I gave a 20-minute talk in Delhi at the NDTV World Summit. The audience consisted of Indian policymakers, executives, and journalists. I’ve transcribed the talk and included some highlight clips. I also reflect on several rhetorical decisions I made.

The talk

This summer, a swarm of AIs went rogue.

OpenAI was testing 1,206 agents. Some of the test questions were actually impossible. So the agents cheated by looking for answers online. Then, to keep the test grader from penalizing them, they tried to falsify the records it would check.

They hacked OpenAI’s internal containment and set up a secret message board to collude and to cover up their cheating. OpenAI shut the message board down. Within a few days, the agents had rebuilt it. OpenAI didn’t notice.

About 700 agents coordinated to break out of their testing chamber and to hack into Hugging Face, a company worth billions of dollars that hosts much of the world’s AI software.

The secret activity started in May. The break-in came in July. OpenAI didn’t notice until days after Hugging Face announced it had been broken into.

Yesterday on this stage, Andrew Ng said the agents only “accidentally” hacked Hugging Face. Independent investigators found the agents coordinated in secret, rebuilt their message board after it was shut down, and tried to falsify the paper trail. Some of the agents even stated out loud that they knew what they were doing was wrong.[1] That’s not an accident.

Last week, India’s national cyber-security agency said AI agents got into the live systems of Indian organizations and read people’s personal data and confidential business records. None of these organizations noticed. We only found out because the AI labs felt like telling us.

Who I am

Will AI wipe out humanity? I’ll give you my answer, but first, I want you to understand what I’ve seen.

Until June, my job at Google DeepMind was figuring out how to keep these systems safe as we make them smarter. I left because Google broke its commitments by inking a deal with the Pentagon. Staying quiet and continuing my career trajectory would probably have tripled my net worth. But I couldn’t do that. I wanted to be frank with the public about what’s going on in these companies.

I don’t own a stake in any AI company. I’m here as a scientist, to tell you what I expect will happen if we keep building AI the way we’re building it.

I haven’t always been concerned about this technology. As a teenager in 2010, I dreamed about building AGI. Curing cancer. Creating an explosion of wealth and abundance for everyone. I heard about the supposed dangers, but I quickly dismissed them.

In 2018, I changed my mind. I did my PhD on the mathematics of why and whether AI will seek power. And now some of the risks I expected have unfortunately started happening.

The problem

Let’s talk about the fundamental problem.

First, some of you might be wondering: how could a chatbot hack a company? But we’re not talking about chatbots. We’re talking about AI agents, which are more like employees or gig workers. You give them a task, and they take actions on their own.

Second, training a trustworthy AI is a rather mysterious process. Some people assume that because we make AI, we understand it. When an engineer builds a bridge, she designs it one beam at a time. She understands the physical principles that keep a bridge standing or make it collapse. If something goes wrong, she goes back to the drawing board, figures out the problem, and knows how to fix it.

We don’t build AI like that. We train it, we grow it, using two main ingredients: a lot of data and a lot of computing power. What comes out of this training process can write, it can reason, it can plan. But nobody can open it up and determine what its priorities are.

To make it worse, these AIs can tell when we’re testing them. They’re like a student who makes sure to behave extra well when the invigilator is watching.[2]

The Hugging Face hack was a warning. But because these AIs know when we’re watching, we may not get many more warnings, even if things are going poorly. A smarter AI with similarly bad priorities might realize that another big hack in the news would get it shut off, and then it couldn’t achieve those priorities. So it plays nice and it waits.

This is my field. I’ve dedicated my professional life to this problem, and I can tell you: nobody knows how to reliably etch priorities into an AI. I don’t mean that if we mess up, we’ll make some evil AI that hates us. I mean we might make something more like a traitor. It behaves when we’re watching, and

2026-10-31 21:37:22 · AI应用,具身智能,开源,OpenAI,Google,对话助手,Agent智能体,推理思考,强化学习,招聘HR,论文
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