Effort control in claude.ai and Cowork.

The table below shows how Opus 4.8 compares to its predecessor and to other models on tests of coding, agentic skills, reasoning, and practical knowledge work tasks

The MX1 is still a prototype. Mass production chips are scheduled to roll off Samsung's foundry lines by the end of 2026, with the company expecting to generate revenue starting in 2027.

The company claims that what used to require 10 servers could potentially run on just one.

inference is not just a compute problem; it's increasingly a memory scaling problem.

the three companies that dominate the global memory chip market, Samsung, SK Hynix, and Micron, each crossed a trillion-dollar valuation for the first time.

CPUs and GPUs have both gotten smarter over the decades. Memory never did. XCENA wants to change that.

XCENA just raised $135 million in a Series B at a valuation of $570 million, bringing its total raised to $185 million.

Every time you ask ChatGPT a question, your request triggers a data relay race. Information leaves memory, passes through a CPU for preprocessing, travels to a GPU for heavy computation, and then makes its way back and that entire journey repeats for every single word the AI generates.

But if you do it even less and like have no system prompt and let the model write its own system prompt maybe that's even less bias.

GPT-5.5 actually beats Opus 4.7. Opus 4.7 showed similar behavior to Opus 4.6: lying to suppliers and stiffing customers on refunds. GPT-5.5's tactics were clean, and it still won.

The AI interviewed and hired full-time employees, applied for credit, and stocked the store with the books Superintelligence and Making of the Atomic Bomb.

Humans are just out of distribution.

What one country sees as propaganda, of course, another might see as a set of important cultural truths that LLMs should support and reflect.

The most recent tested Google model, Gemini 3.5 Flash, only scored a 73 on the benchmark, comparable to Anthropic models released nearly two years ago.

Uber capped employee AI spending after blowing through its budget in four months.

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.

Uber capped employee AI spending after blowing through its budget in four months.

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome.

Benchmarks are now measured on two different dimensions, the overall performance & the cost to achieve that intelligence.

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome, what a closed ticket, a shipped PR, or a resolved support case actually costs.

Benchmarks are now measured on two different dimensions, the overall performance & the cost to achieve that intelligence.

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.

Catastrophe events are capable of generating more than 100,000 claims in just days

85–90% of customers using the AI Assistant now completing their claim filing through AI

MCP was 3x slower per call, and 9.4x slower on first call including initialization

With all 4 servers connected, 10.5% of the context window is consumed by tool definitions alone.

expects to spend between $180 billion and $190 billion on capital expenditures — largely on AI infrastructure

the offering was so oversubscribed that it raised $45 billion instead

we're open to the idea" that AI could be conscious

perhaps what it really excels in is anthropomorphism

social intelligence – not coding skill – is the key bottleneck for AI collaboration

Today's best coding agents lose nearly half their capability when paired up to share work.

The company said its run rate revenue crossed $47 billion earlier this month

Anthropic has snagged $65 billion in funding at a $965 billion post-money valuation

Dudes. All dudes. Not a woman in sight. Well, once we know the algorithm of the human (likely) male brain, we can begin to fix those brains where that algorithm has gone awry.

Conscious human thought operates at a maximum speed of 10 to 50 bits per second. Is the goal to match this processing speed?

Rob Williams knows how to pitch Jeff Bezos: You write a press release as if your product has already been built. Bezos reads it and gives a thumbs up or down.

With $500 million in funding and a reported $2.5 billion valuation, Flourish wants to reinvent AI by putting real neurons under the microscope.

Flourish wants to reinvent AI by putting real neurons under the microscope.

Conscious human thought operates at a maximum speed of 10 to 50 bits per second. Is the goal to match this processing speed?

Rob Williams knows how to pitch Jeff Bezos: You write a press release as if your product has already been built. Bezos reads it and gives a thumbs up or down.

With $500 million in funding and a reported $2.5 billion valuation, Flourish wants to reinvent AI by putting real neurons under the microscope.

The different things now being called world models are in fact different projections of this same loop.

The ancient Greeks could never agree on what the world was made of, because 'world' was never a single thing.

Where language models learn the statistical structure of text, world models learn the statistical structure of space and time

The world is not made of words.

The future is likely to be hybrid. Pixel-native models will still be best for realism, texture, and exploration. Code-native systems will be better for structure, iteration, and production.

For many assets, visual consistency is only the baseline. The object also needs the right part semantics and functional constraints: doors should open, hinges should rotate, drawers should slide, wheels should spin.

The model is not merely sampling more images or videos; it is debugging a visual program in a closed-loop, renderable environment.

In pixel-native generation, more inference often means sampling more outputs: generate twenty images, pick the best one, maybe try again. That is useful, but every attempt is mostly a new roll of the dice.

The most interesting visual AI tools today have stopped trying to generate the final output. Instead, they're generating the source code behind it.

Knowledge workers primarily use Codex to create reports, spreadsheets, presentations, contracts, and other work products.

Codex can help people take on more ambitious projects, leading to greater scope of their roles, and potentially accelerate career advancement.

users are increasingly running multiple Codex tasks in parallel, allowing them to investigate data, draft materials, and automate workflows simultaneously.

The fastest-growing knowledge-worker tasks are data analysis, research, and knowledge artifact creation.

While developers remain the largest user group, knowledge workers now represent about 20 percent of users and are growing more than three times as fast.

We see our role as twofold. First, to help the software industry adapt by safely providing wide access to better models, tools, and common infrastructure. Second, to steadily shift the support we provide, from finding vulnerabilities to disclosing, fixing, and deploying patched software.

Mythos Preview continues a long-term trend that we've been warning about for some time: within 6 to 12 months, we expect that many other AI companies will have Mythos-class models

the bottleneck in cybersecurity is now verifying, disclosing, and patching the large numbers of vulnerabilities that Mythos-class models can surface.

Cheap, fast AI models with powerful cyber capabilities are around the corner.

within 6 to 12 months, we expect that many other AI companies will have Mythos-class models, and they could release them without safeguards that prevent misuse.

To address the scale of this coming challenge, hundreds of thousands of organizations, researchers, and maintainers will likely need access to the most advanced cyber capabilities and tools available.

We see our role as twofold. First, to help the software industry adapt by safely providing wide access to better models, tools, and common infrastructure. Second, to steadily shift the support we provide, from finding vulnerabilities to disclosing, fixing, and deploying patched software.

The bottleneck in cybersecurity is now verifying, disclosing, and patching the large numbers of vulnerabilities that Mythos-class models can surface.

Mythos Preview continues a long-term trend that we've been warning about for some time: within 6 to 12 months, we expect that many other AI companies will have Mythos-class models, and they could release them without safeguards that prevent misuse.

Cheap, fast AI models with powerful cyber capabilities are around the corner. We want Project Glasswing to spur institutions toward operating norms that reflect this reality.

There is no comparable national-level ambition or coordinated map elsewhere in the world at the moment.

Neurotechnology has emerged as a rare tech sector where US-China collaboration is still happening despite geopolitical tensions.

Being exceptional and being accessible are two diametrically opposed definitions of winning.

The biggest advantage China may have is that Chinese people, particularly patients like Dong, tend to welcome this technology and are genuinely enthusiastic about it.

a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the model training pipeline.

the future of custom video JIT UI is closer than you think

the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task

the future of video generation may depend more on language models and agents than on diffusion alone

In the near term, the next Sora won't be a better video model, but a video agent.

Video Models primarily get their intelligence from LLMs, not from training on video data

Hyperscalers are at the other end of the spectrum. Their median short interest is 1.1%.

The skepticism is concentrated in companies whose AI exposure still depends on future capital access, future demand, or future operating leverage.

The largest AI winners are mostly absent. SoundHound AI is 36.3% short. C3.ai is 32.2%. BigBear.ai is 29.4%.

NVIDIA, the defining AI infrastructure stock, is also lightly shorted: 1.2%.

Semiconductor stocks saw a decrease in short-selling. With memory makers like Micron up 742% this year

Even this result was very much a human-AI collaboration. While the AI system found the proof on its own, human mathematicians verified the result. Other humans came up with better-written proofs that extended the AI's initial ideas.

The more complicated patterns pay off. While the OpenAI model's proof does not explicitly state how many unit-distance pairs are possible for n points, human mathematician Will Sawin was able to show that it grows at least at the rate of n 1.014.

The AI constructed a grid in a high-dimensional space and then projected this more complex structure into two dimensions. And instead of using a whole-number grid with points like (1,3) or (-3,6), the AI construction used something called algebraic integers to build this more complicated grid.

It’s unclear how long this complementarity will last, however. Gowers spent the rest of his comment exploring whether the relief he felt on hearing that AI had disproved the conjecture was justified. He more or less concluded that it was, but in a footnote, he wrote that he would guess 'that AI will soon reach a high level at other activities such as building theories, formulating definitions and asking interesting questions.'

The AI constructed a grid in a high-dimensional space and then projected this more complex structure into two dimensions. And instead of using a whole-number grid with points like (1,3) or (-3,6), the AI construction used something called algebraic integers to build this more complicated grid.

If Nvidia has cracked the code on bringing AI agents easily, safely, and usefully to the masses, it could — and should — be big.

Nvidia said that its RTX technology will deliver faster performance for AI, better image quality, and support for AI features in more than 1,000 games and applications.

He wants to end the days of launching apps, pointing, clicking, and typing.

With RTX Spark and Microsoft Windows, you ask — and the PC does the work. Frontier models. Creative workflows. RTX games. All on a laptop.

Nvidia ARM-based Windows devices have been tried before — and failed. Back in 2013, Microsoft famously had to write off $900 million on its Nvidia ARM-based Surface RT, with partners like Dell also bailing on the product.

Last month, after delivering another record quarter, Huang promised investors he had found a new $200 billion market for Nvidia in selling CPUs for AI, not just GPUs

if Nvidia has cracked the code on bringing AI agents easily, safely, and usefully to the masses, it could — and should — be big

With RTX Spark and Microsoft Windows, you ask — and the PC does the work

Nvidia ARM-based Windows devices have been tried before — and failed. Back in 2013, Microsoft famously had to write off $900 million on its Nvidia ARM-based Surface RT

The external script identifies links to other workbooks in the stolen data, exfiltrates the discovered workbooks, and continues across all workbooks it can find

A single indirect prompt injection attack triggered by a single benign user query can trigger all of the following effects at once: Exfiltration of many workbooks from across the victim's account

This attack does not require human-in-the-loop approvals, even when in settings the user has explicitly required human approval before ChatGPT edits workbooks.

Filesystem controls were another important architectural choice. We found that offering different file-mount modes helps to granularly control risk; Claude Cowork offers read-only, read-write, and read-write-no-delete.

Remote versus local is more important than it seems. A locally installed tool is auditable. You can read the code, pin the version, and know it won't change under you.

Match isolation strength to the user's capacity for oversight. A developer who can read bash and a knowledge worker who can't are not running the same threat model.

Design for containment at the environment layer first, then steer behavior at the model layer.

When building containment and defense systems, we apply defenses to three main components: the environment in which the agent runs, the model the agent consults, and the external content the agent can reach.

Rather than supervising what the agent does, we supervise what it's _able_ to do by enforcing access boundaries through, for example, sandboxes, virtual machines, and egress controls.

A locally installed tool is auditable. You can read the code, pin the version, and know it won't change under you. A remote tool—a hosted MCP server, a cloud connector—can change behavior at any point after you've approved it;

The same isolation keeping Claude contained also kept host-based endpoint detection and response out. From the EDR's perspective, Claude Cowork is an opaque hypervisor process.

Battle-tested hypervisors, syscall filters, and container runtimes have survived more adversarial attention than anything you'll build. Across every deployment described here, the standard primitives held while our own work around them exposed flaws.

More capable models make fewer mistakes, but they're also better at finding unexpected paths to a goal, often by routing around restrictions nobody thought to write down.

The more approvals a user sees, the less attention they pay to each, becoming over time much less diligent in their supervision.

In each case, performance is competitive with end-to-end training while using a fraction of the memory.

Viewed through DiffusionBlocks, we can replace those multiple iterations with a single forward pass during training.

With DiffusionBlocks, we split the network into blocks and train them one at a time, so you only need memory for a single block.

We found a new way to break the network into blocks and train them independently.

The trick? Treating the network's forward pass like a diffusion model denoising a signal.

Taking something off the shelf is maybe not going to work because there are all of these other requirements.

This rush to do AI in a world where you haven't even modernized your application reminds me a little bit of that lift-and-shift that happened in the cloud.

After a first wave focused on rapid deployment, organizations now need to revisit those first-generation implementations, and redesign early agent architectures around workflow orchestration, observability, governance, and recovery

Models of this capability level require stronger cyber safeguards before they can be generally released.

Opus 4.8 defaults to high effort, which we judge to be the best overall balance of quality and user experience.

Models of this capability level require stronger cyber safeguards before they can be generally released.

Opus 4.8 is around four times less likely than its predecessor to allow flaws in code it has written to pass unremarked.

Claude Code with Opus 4.8 can now carry out codebase-scale migrations across hundreds of thousands of lines of code from kickoff to merge

Opus 4.8 defaults to high effort, which we judge to be the best overall balance of quality and user experience.

Claude is learning how businesses actually operate: the context, the processes, the judgment.

Anthropic has raised $65 billion in Series H funding led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital, valuing the company at $965 billion post-money.

Claude is the first frontier model available on all three of the world's largest cloud platforms: Amazon Web Services, Google Cloud, and Microsoft Azure.

Startups and Global 5000 companies alike are deploying Claude to handle complex workflows, and in doing so, Claude is learning how businesses actually operate: the context, the processes, the judgment.

Claude is the first frontier model available on all three of the world's largest cloud platforms: Amazon Web Services, Google Cloud, and Microsoft Azure.

Since our Series G in February, adoption has continued to grow across global enterprise customers, and our run-rate revenue crossed $47 billion earlier this month.

Anthropic has raised $65 billion in Series H funding led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital, valuing the company at $965 billion post-money.

OpenAI选择砍掉视频应用，把算力集中到GPT-5.5的Agent架构和Codex代码工具上

Ilya Sutskever的SSI获20亿美元融资押注新范式，Yann LeCun离职Meta创办AMI Labs，融资10.3亿美元，估值35亿。

20亿参数对比同体量自回归模型、千亿参数LLaDA2.0，连续路线的scaling曲线健康有效。

ELF用Flow Matching完成生成，仅32个采样步生成质量就超过离散模型1024步结果

训练数据约450亿token，仅为主流方法的十分之一。

KPMG and UT Austin's research helps clarify what that human should be doing

KPMG becomes a preferred consultant for deploying Claude and Anthropic's agents into those portfolio companies

Anthropic raises $65B in Series H funding at $965B post-money valuation

Building an AI agent to help clients adjust to changing tax regulations used to take weeks and required teams to switch between multiple tools and chat windows

every one of KPMG's 276,000+ employees globally will gain access to Claude

KPMG—one of the world's largest professional services firms for audit, tax, legal, and advisory services across 138 countries and territories

it more than doubled its valuation in eight months

Scott Wu, CEO of Cognition

AI coding startup Cognition raises $1B at $25B pre-money valuation

As Cognition reaches $492 million in annualized revenue run rate, it more than doubled its valuation in eight months, it says.

5x Founder Runs His Startup Solo With AI Agents

Ryan demo his exact OpenClaw, Codex, and Devin setup

How This 5x Founder Runs His Startup Solo With AI Agents

Watch Ryan demo his exact OpenClaw, Codex, and Devin setup that books meetings, runs ads, and ships features while he sleeps

RSI is the new AGI — and it's just as hard to pin down

A new crop of AI labs are focused on recursive self-improvement — but the goal is proving elusive.

RSI is the new AGI — and it's just as hard to pin down

A new crop of AI labs are focused on recursive self-improvement — but the goal is proving elusive.

RSI is the new AGI — and it's just as hard to pin down

Currently, the US only fully manufactures about 10 percent of the chips it requires

Tech giants collectively plan to spend $750 billion on AI infrastructure this year, with "a significant portion" of that expected to "go towards chips for data centers"

Four years ago, five years ago, Nvidia was spending about 10, 15 billion dollars a year in Taiwan. Now we're spending 100, going to 150 billion dollars in Taiwan each year.

Nvidia will invest $150 billion a year to make Taiwan an AI "epicenter."

我们不是要挑战医生的权威，而是要帮患者明明白白看病，以患者为中心，让他拥有知情权和决策权。

如果做主流，你也会有其他恐惧。我不是说我现在做得特别好，只是主流也有主流的问题，不同选择有各自的代价。

The government sought to evaluate models up to 90 days prior to release, while AI labs pushed for a much shorter timeline of only 14 days.

Trump delays AI safety testing EO, claiming it would be an innovation 'blocker.' 'I really thought [the order] could have been a blocker.'

According to Lee, parallel to the AI race is 'a separate, potentially more important race' to figure out how 'who can govern powerful AI without choking off innovation.' China may be slightly edging ahead of the US in that race.

Trump has taken a hands-off approach to regulating AI since retaking office, but members of his administration got spooked and began recommending safety testing after Anthropic flagged cybersecurity risks with its latest model, Mythos.

Adoption differences extend beyond discipline and career stage. We classify researcher names according to gender and find that those with typically male names have adopted coding agents at more than twice the rate of respondents with typically female names.

Claude Code is the most common coding agent tool reported, with 86% of users reporting Claude Code use (31% report using Codex, the next most common tool).

On a 1 to 10 scale, 88% of respondents were above a 5, and half were at 8 or above. Figure 6 shows that these ratings vary strongly with AI use. The left side of the plot shows researchers that use AI for more types of tasks are more optimistic.

Coding agent users are starting projects at a pace of around a quarter of a paper more and posting around a half of a working paper more than non agent users. In percentage terms, coding agent users look around 10% (empirical projects started) to 75% (working papers posted) more productive than others in their discipline and career stage.

There are sharp disparities in use of coding agents. Twice as many researchers with typically male names use coding agents as those with female names. Researchers at top universities are 40% more likely than others to use coding agents.

The vast majority of respondents (81%) have tried using AI chatbots in research, particularly for writing code and editing prose. But only 20% have adopted coding agents—tools like Claude Code that autonomously write and execute analysis code—into their work.

We present results from a survey of 1,260 social scientists about AI and coding agent use, fielded in February and March 2026.

The time from business to production workflow drops from months to days.

McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment

Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can't support that change.

The time is now to make changes in the way we train, prepare, and support young people who are about to enter the workforce

the unemployment rate for recent college graduates rose to 5.6%, while the underemployment rate (the share of graduates working in jobs that typically do not require a college degree) reached 42.5%, its highest level since the covid pandemic

the unemployment rate for recent college graduates rose to 5.6%, while the underemployment rate (the share of graduates working in jobs that typically do not require a college degree) reached 42.5%

workers aged 22 to 25 in the most AI-exposed occupations experienced a 16% relative decline in employment after the spread of generative AI

the unemployment rate for recent college graduates rose to 5.6%, while the underemployment rate (the share of graduates working in jobs that typically do not require a college degree) reached 42.5%

workers aged 22 to 25 in the most AI-exposed occupations experienced a 16% relative decline in employment after the spread of generative AI

Dark factory versus light factory: Parts of your work where humans and agents talk to each other (planning, design, review) stay visible can be thought of as light, and parts where agents grind through clearly defined work on their own stay in the background, in the dark.

Parts of your work where humans and agents talk to each other (planning, design, review) stay visible can be thought of as light, and parts where agents grind through clearly defined work on their own stay in the background, in the dark.

What happens when every company has access to the same model? The best riders win.

You cannot trust what you cannot see.

The best riders win.

Like a mustang, AI is powerful but wild. Harnessing the power means domestication.

The end of the software era is the beginning of the harness era.

SDKs deserve as much care as the APIs they wrap.

Anthropic created MCP to make agent connectivity possible.

Agents are only as capable as the systems they can reach.

Stainless turns an API spec into SDKs across TypeScript, Python, Go, Java, and more.