agentic systems can be designed to call on such tools when they might be useful

For the next decade or so, we should think about AI as this amazing tool to help scientists

general-purpose reasoning model in the vein of GPT-5.5

Google fellow John Jumper, who won the Nobel for AlphaFold, is now working on AI coding, not on science-specific AI tools

We have been watching what developers have built on Claude over the last few years, which made bringing our teams together an easy decision.

Anthropic created MCP to make agent connectivity possible.

Agents are only as useful as what they can connect to.

SDKs deserve as much care as the APIs they wrap.

The frontier of AI is shifting from models that answer to agents that act—and agents are only as capable as the systems they can reach.

In my opinion this paper demonstrates that current AI models go beyond just helpers to human mathematicians – they are capable of having original ingenious ideas, and then carrying them out to fruition.

The key ingredients of the construction come from a very different part of mathematics known as algebraic number theory, which studies concepts like factorization in extensions of the integers known as algebraic number fields.

The proof came from a new general-purpose reasoning model, rather than from a system trained specifically for mathematics, scaffolded to search through proof strategies, or targeted at the unit distance problem in particular.

An internal OpenAI model has disproved this longstanding conjecture, providing an infinite family of examples that yield a polynomial improvement.

The precise argument uses tools such as infinite class field towers and Golod–Shafarevich theory to show the number fields required for the argument actually exist. These ideas were well-known to algebraic number theorists, but it came as a great surprise that these concepts have implications for geometric questions in the Euclidean plane.

SIMA 2 An agent that plays, reasons, and learns with you in virtual 3d worlds

Anthropic leads OpenAI in business adoption, according to Ramp.

annualized revenues approaching $50 billion – a fivefold increase in as many months.

90% of finance reporting is now AI-driven as well.

Chinese AI labs have developed an efficiency moat that may define the AI market's development over the coming years.

there are around 10,000 people— founders and employees at companies like OpenAI, Anthropic, and Nvidia — that have 'hit retirement wealth of well above $20M'

the same technology is both the lottery ticket & the thing eating your fallback

The vibes around the current AI boom aren't great, even in the tech industry

We believe AI can meaningfully expand what's possible for the smallest businesses, including solo entrepreneurs.

Small businesses account for 44% of U.S. GDP and employ nearly half the private-sector workforce, but their adoption of AI has lagged behind larger enterprises.

Small businesses need AI that moves at the speed they do. With Canva powering content creation in Claude for Small Business, a business owner can go from idea to published, on-brand design in one flow

What we used to think were the constraints are just not constraints anymore. It's empowering. Hours of looking at stuff that doesn't matter are gone.

We don't train on your data by default on our Team and Enterprise Plans.

AI is the first technology that can finally close that gap, which is why we're launching Claude for Small Business

It's very enticing to say we're just going to replace everything with a chatbot, but it's not changing the bottom line.

Frankly, no customer ever just wants to talk to your chatbot.

Willis said there's no magic for innovating. Companies need to do the hard work of understanding how AI may or may not be useful for the desired outcome.

The deeper problem, he said, is that companies are treating AI itself as a solution rather than as a tool to help power the solution.

What company leaders face, he said, is not an innovation problem but an impatience problem.

YouTube commenters started naming the robots Bob, Frank, and Gary yesterday, so we added name tags to each robot

If a robot has a software or hardware issue, it autonomously leaves for maintenance and another robot takes over.

If the robot gets stuck or the AI policy goes out of distribution, Helix triggers an automatic reset.

There is no teleoperation - every action comes directly from Helix-02

The robots are reasoning directly from camera pixels

Humans average around 3 seconds per package. F.03 is now around human parity.

When you stop using the agent, all the productivity benefit goes away... but the added maintenance costs don't!

If you want a productive team, you have to focus on their maintenance costs.

For every month you spend writing code, you'll spend some amount of time in the following year maintaining that code, and some in each year after that, forever, as long as that code exists.

occasionally even identifying the benchmark

Models sometimes recognize they're being evaluated

New research from @AISecurityInst and Goodfire

meaning safety benchmarks may not reflect real-world behavior

We show this verbalized eval awareness inflates safety scores

Models sometimes recognize they're being evaluated, occasionally even identifying the benchmark.

AI doesn't own state transitions. The Bubble Tea architecture has a beautiful idea: Update() is the only place state mutates, driven by messages.

AI generates this pattern because it's the shortest path from 'fetch data' to 'render table.'

The complexity was accumulating invisibly while the velocity metric said 'you're shipping!'

AI writes features, not architecture. The longer you let it drive without constraints, the worse the wreckage gets.

The velocity makes you think you're winning right up until the moment everything collapses simultaneously.

The tl;dr of this dev log is that I still need to be in the loop to make anything meaningful.

When combined with Vantor's automated spatial fusion and production capabilities, this approach allows organizations to build analytics pipelines and process multi-sensor data in near real-time—detecting objects of interest, identifying patterns of change, and describing activity with operational context in secure, sovereign mission environments.

Collectively, this foundation represents an unmatched planetary-scale dataset for AI systems.

Tensorglobe enables training and fine-tuning of Earth AI models locally with a customer's own sensor data and private archives.

This integration marks the first time Earth AI imagery models have been deployed commercially against a dataset with the scale, accuracy, and temporal depth of Vantor's AI-ready spatial foundation.

Vantor becomes the first spatial intelligence company to be able to deploy Google Earth AI models in air-gapped government environments.

ForestCast, the first deep learning benchmark for proactive deforestation risk forecasting, is a model that utilizes pure satellite data to predict future forest loss accurately and at scale, overcoming the limitations of older methods that relied on inconsistent, region-specific input maps.

WeatherNext is an AI-powered ensemble forecasting model for global weather prediction. It utilizes a novel Functional Generative Network architecture, which enables it to generate forecasts 8x faster and with resolution up to 1-hour.

Open Buildings uses AI to put everyone on the map

Groundsource uses Gemini to analyze decades of public reports and identifies over 2.6 million historical flood events spanning more than 150 countries.

Breakthroughs in understanding the Earth that previously required complex analytics and years of iteration are now made possible in a matter of minutes.

We don't train on your data by default on our Team and Enterprise Plans.

Small and mid-market businesses fuel our economies, and for decades, QuickBooks has been proud to be their trusted financial partner.

Tools and training are rarely tailored to the ways small businesses operate, and as a result their use often stops at the chat window.

AI is the first technology that can finally close that gap, which is why we're launching Claude for Small Business

In quantum physics, AlphaEvolve's optimizations have made it possible to run complex molecular simulations on Google's Willow quantum processor by suggesting quantum circuits with 10x lower error than previous conventionally optimized baselines.

AlphaEvolve improved the efficiency of Google Spanner by refining its Log-Structured Merge-tree compaction heuristics. This optimization reduced 'write amplification'—the ratio of data written to storage versus the original request—by 20%.

Tools such as AlphaEvolve are giving mathematicians very useful new capabilities. For optimization problems in particular, we can now quickly test potential inequalities for counterexamples, or to confirm our beliefs in what the extremizers are, which greatly improves our intuition about these problems and allows us to find rigorous proofs more readily.

AlphaEvolve began optimizing the lowest levels of hardware powering our AI stacks. It proposed a circuit design so counterintuitive yet efficient that it was integrated directly into the silicon of our next-generation TPUs.

Frontier AI labs are often described as being in a 'race'. I'm not sure what exactly they're racing toward, but it often seems to involve automating huge swathes of human labor, a prize potentially worth tens of trillions of dollars a year — if you win.

If a 100× pay gap is driven by a 100× researcher quality gap, then simulating a top researcher might speed things up much more than simulating an average researcher. But this isn't the case if much of the pay gap is driven by the superstar dynamic — the gap in researcher quality might actually be much smaller.

The problem with this explanation is that it's very incomplete. In reality, we should expect to see big differences in pay even if superstars were only a tiny bit better than your average postdoc.

Are there transparency regimes and tools that can enable a broad set of people, not just frontier AI companies, to easily study real-world AI usage?

If an intelligence explosion was upon us, what intervention points would facilitate slowing or otherwise changing the rate of the explosion? Assuming humans can intervene, which entities should wield this capacity—governments? Companies?

If AI substantially reduces the centrality of paid work in human life, what conditions will allow people to reallocate their time and effort toward other sources of meaning, and what can we learn from historical or contemporary populations where work has been scarce or optional?

It demonstrated incredible generalization. Without any retraining, TRINITY transferred zero-shot to four unseen tasks

This foundational research is part of the core engine powering our multi-agent product: Sakana Fugu

We believe the future of AI isn't just about scaling monolithic models, but engineering collaborative, diverse AI ecosystems that can adapt and combine their strengths.

TRINITY transferred zero-shot to four unseen tasks (AIME, BigCodeBench, MT-Bench, and GPQA). On average, the evolved coordinator surpassed every individual constituent model in its pool, including GPT-5, Gemini 2.5-Pro, and Claude-4-Sonnet.

We found that evolution is uniquely suited to optimize this tight, high-dimensional coordination problem where traditional gradient-based methods fail.

Imitation learning (Supervised Fine-Tuning) was ruled out because generating multi-turn labels is prohibitively expensive.

Traditional Reinforcement Learning (REINFORCE) failed because the gradients had a low signal-to-noise ratio due to binary rewards and weak parameter coupling.

The coordinator relies on the hidden states of a compact language model and a small routing head. In total, it has fewer than 20K learnable parameters.

While model merging offers a way to combine different skills, it is often impractical due to mismatched neural architectures and the closed-source nature of top-performing models.

In nature, complex problems are rarely solved by a single monolithic entity, but rather by the coordinated efforts of specialized individuals working together.

What if instead of building one giant AI, we evolved a coordinator to orchestrate a diverse team of specialized AIs?

An FPGA with the weights in memory and a wire looping output back to input could just sit there, executing SUBLEQ programs. Just a transformer being a transformer being a computer.

The 100:1 loss trick. In a 33 long sequence, only 2 positions change per step. Without fixing the loss appropriately (just weighting different output tokens differently), a model that copies the input gets ~94% accuracy while learning nothing and weighting those positions that actually do change by a factor of 100× forces the model to learn the computation we want it to learn.

Almost every error is a copy error. The model has 100% accuracy on positions that actually change so it learned SUBLEQ perfectly but it just occasionally dropped a value when routing ~30 unchanged mem cells through attention.

Width, not depth, is the bottleneck. A wide model (d=256, 6 layers, 4.9M params) dramatically outperforms a deep model (d=128, 12 layers, 2.4M params). SUBLEQ execution requires routing 32 mem values through attention simultaneously and width helps for that.

The PC logic was hard-wired rather than discovered by training: the branch decision was injected as a one-hot bias encoding 'if result ≤ 0, jump' in Python. The write was rounded and clamped to int, then converted to bytes.

When you train a model to add, it learns one function. When you train a model to sort, it also learns one function. When you train a model to execute SUBLEQ, it learns... every function? Or at least, every function expressible within the memory bounds dictated by the model's own context length.

A trained SUBLEQ transformer would be the first computer found by gradient descent, on a generic architecture not designed to be a computer, and with weights not hard-crafted by a person.

The thing that impressed me the most about GPT-3 was this: I gave it a weird mix of matlab and python code with a few variables, a loop, some basic arithmetic. Nothing fancy and I knew this kind of thing was probably in the training data, but for shure not with these exact numbers and variables.

GUI bottleneck (Gemini spent weeks unable to list a product due to misclicking)

Most passing SWE-Bench solutions are not accepted by maintainers.

Whatever is precise enough to benchmark is also precise enough to optimize for.

Our partnerships with Accenture, Deloitte, PwC, and the other consulting and systems integration firms in the Claude Partner Network are one of the ways Claude benefits the world’s largest enterprises today.

The clinicians know where time disappears in a shift and what good patient care actually requires.

Enterprise demand for Claude is significantly outpacing any single delivery model.

Companies from community banks to mid-sized manufacturers and regional health systems stand to gain from AI, but lack the in-house resources to build and run frontier deployments.

If most efficiency improvements came from a small handful of scale-dependent innovations, then existing models of the software intelligence explosion may be flawed.

none explicitly account for training compute scaling being a source of software progress, so they could heavily overstate the importance of research effort.

Researchers have been throwing tons of effort into getting better training data. For example, Surge AI had a revenue of over $1 billion last August, and Scale AI was probably in a similar boat.

the error bars look almost comically wide in the graph above — across the different estimates, they range from around 1.1× to 300× per year!

Almost all the evidence points to very fast software progress: each year, the training compute needed to get to the same capability declines several times — possibly even ten times or more.

AI software progress is about reducing the training compute you need to get to the same level of capability, through better algorithms or data.

an ARA-native review system that automates objective checks so human reviewers can focus on significance, novelty, and taste.

We introduce the Agent-Native Research Artifact (ARA), a protocol that replaces the narrative paper with a machine-executable research package structured around four layers

On RE-Bench's five open-ended extension tasks, preserved failure traces in ARA accelerate progress, but can also constrain a capable agent from stepping outside the prior-run box depending on the agent's capabilities.

Tolerable for human readers, these costs become critical when AI agents must understand, reproduce, and extend published work.

Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way.

The one real underlying asset, Workday's trillion-transaction dataset, is thinner than it sounds; what actually matters at runtime is how data connects to workflows, permissions, and integrations, and every layer of that stack is now a liability.

When customers renew at close to 100% every year, it's usually read as a sign the product is delightful. In Workday's case, it's a sign of something else: leaving is close to impossible.

We also learned that treating agents as rigid nodes in a state machine doesn't work well. Models get smarter and can solve bigger problems than the box we try to fit them in.

Our early versions of agentic work was only asking Codex to implement the task. That approach proved too limiting. Codex is perfectly capable of creating multiple PRs as well as reading review feedback and addressing it.

When our engineers no longer spend time supervising Codex sessions, the economics of code changes completely. The perceived cost of each change drops because we're no longer investing human effort in driving the implementation itself.

Among some teams at OpenAI, we saw the number of landed PRs increase by 500% in the first three weeks.

Six months ago, while working on an internal productivity tool, our team made a controversial (at the time) decision: we'd build our repo with no human-written code. Every line in our project repository had to be generated by Codex.

Symphony also shines in large multi-agent workflows, where multiple agents work together on a single task.

Instead of using domain knowledge to prescribe team organization, roles, or workflows, Fugu learns to dynamically assemble agents from a pool and coordinate them through non-obvious but highly efficient collaboration patterns.

The depth of recursion becomes a tunable compute axis at inference time, requiring no retraining. A small model, by reading itself, can iterate toward answers that neither it nor any of its workers could reach in a single pass.

We estimate, with 90% confidence, that between 290,000 and 1.6 million H100-equivalents of compute were smuggled through the end of 2025.

The biggest driver of uncertainty on the diversion side is that we don't know what fraction of diversion has been observed. The large-scale smuggling schemes detected and reported so far could represent the majority of the volume, or they might be just a small fraction of the total flows.

We estimate that between 290,000 and 1.6 million H100-equivalents (H100e) were smuggled to China through 2025. Our median estimate of 660,000 H100e would be roughly a third of China's total compute.

we are nearing the “end of the exponential” for AI development

The protocol accounts for this in two ways. When an agent provisions a paid service, Stripe includes a payment token in the request to the Provider (Cloudflare).

The agent has gone from literal zero, no Cloudflare account at all, without any preconfigured [Agent Skills](https://github.com/cloudflare/skills) or [MCP server](https://blog.cloudflare.com/code-mode-mcp/), to having: * Provisioned a new Cloudflare account * Obtained an API token * Purchased a domain * Deployed an app to production

The entire AI community should be able to easily access the full capabilities of TPUs, and because many of these potential users build models in PyTorch, an integration that allows PyTorch to work natively and efficiently on the TPU is crucial.

Of course it’s impossible to know for sure, but I think I really wouldn’t. Even the ideal version, industrial megaprojects at hyperhuman scale while constantly being out over your skis with leverage sounds hellish.

The software engineers who will be most valuable in the future are not the ones who do everything themselves. They are the ones who refuse to spend time on work that A.I. can do for them, while still understanding everything that is done on their behalf.

It’s feeling like a new form of [AI psychosis](https://en.wikipedia.org/wiki/Chatbot_psychosis).

An OpenAI investor told Axios that the shift could benefit them, since they view Codex as superior to Claude Code at maximizing tokens efficiently, cutting down on usage costs.

The way they see it, cryptographically relevant QCs _will_ plausibly be built sometime soon: indeed, it’s ultimately unavoidable, even if people’s only interest in QC was to do quantum simulations for materials science and chemistry.

The practice is emblematic of Silicon Valley’s newest form of conspicuous consumption, known as “tokenmaxxing,” which has turned token usage into a benchmark for productivity and a competitive measure of who is most AI native.

Employees at Meta Platforms who want to show off their AI superuser chops are competing on an internal leaderboard for status as a “Session Immortal”— or, even better, “Token Legend.”

Anthropic today quietly (as in _silently_, no announcement anywhere at all) updated their [claude.com/pricing](https://claude.com/pricing) page (but not their [Choosing a Claude plan page](https://support.claude.com/en/articles/11049762-choosing-a-claude-plan), which shows up first for me on Google) to add this tiny but significant detail (arrow is mine, [and it’s already reverted](https://simonwillison.net/2026/Apr/22/claude-code-confusion/#they-reversed-it)):

the top conversations we have been hearing from AI leadership (CTOs, VPs, Founders) have all centered around the concept of “Tokenmaxxing” and how leaders want to get their teams using more AI, WITHOUT the downside of incentivizing the kinds of horrendous waste

Contrary to predictions, motivated investor framing did not suppress AI fraud warnings; if anything, it marginally increased them.

When mobile phones became widespread, gathering data about people got much cheaper, but making use of that data remained difficult. Powerful LLMs could change that.

LLM agents could potentially do the work of intelligence analysts in a fraction of the time and for a fraction of the cost, which would enable the state to aim its all-seeing eye toward anyone, not just its highest-priority targets.

Claude skews high-income; Meta AI skews low-income

Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data.

There is an aspect of entrepreneurship where you’re rewarded for selling a vision of what could be, and it doesn’t always get realized.

The true founders—not the ones who want to make a lot of money or do it because their roommates want to do it—are closer to artists than to any other profession.

The smartest companies are no longer just hiring talent; they are purchasing synthetic intelligence by the gigawatt.

The smartest companies are no longer just hiring talent; they are purchasing synthetic intelligence by the gigawatt.

The issue for many people isn’t the technology itself (though there are many ethical issues in how it was trained). The issue is the stupid state of our capitalist system, and the weird way companies are trying to force it down everyone’s throats.

All of knowledge work has this problem. It's hard to objectively judge the quality of someone's work without spending a lot of effort on it. Therefore everyone relies heavily on proxy measures.

Critics called the manifesto [fascist](https://bsky.app/profile/gilduran.com/post/3mjwqsyj54s2a)

Here, he’s been consistent; in March 2024 Karp told a CNBC reporter that ‘if you have a position that does not cost you ever to lose an employee, it’s not a position’

Karp gave an interview to CNBC claiming that AI could undermine the power of ‘humanities-trained—largely Democratic—voters’ and increase the power of working-class male voters

At one point during the call, one of the employees tried to level with the group, explaining that Palantir’s work with ICE was a priority for Karp and something that likely wouldn’t change any time soon.

We were supposed to be the ones who were preventing a lot of these abuses. Now we're not preventing them. We seem to be enabling them.

Palantir was founded—with initial venture capital investment from the CIA—at a moment of national consensus following the September 11, 2001, attacks

Last fall, Palantir seemed to become the technological backbone of Trump’s immigration enforcement machinery, providing software identifying, tracking, and helping deport immigrants on behalf of the Department of Homeland Security

Will smarter models be increasingly expensive because of greater accuracy or less expensive because they're smarter?

Then Opus 4.7 shipped & the smarter model became much more expensive. The cause : a new tokenizer

When Anthropic launched Opus 4.5 in November 2025, the bigger, more expensive model was actually cheaper to use.

The agent interprets new information and adapts the logic. The engine applies that logic continuously and emits precise updates.

Agents and CDC streams are powerful together because they split the work well.

The fix is not smarter prompts. It is software built to meet agents halfway.

Humans are not a good target for calm technology.

Today's agents, the copilots, the chatbots are designed to be human like.

Meanwhile, in reality, the only 'official' MeshCore is the github repo. It's the source of truth in terms of what is MeshCore, and Andy has never contributed to that.

Since inception, the MeshCore development team have been working hard to build MeshCore. We've released more than 85 versions of the MeshCore Companion, Repeater and Room Server firmwares with support for more than 75 hardware variants. All of this has been hand crafted, by humans.

Andy Kirby did do an amazing job helping to promote the MeshCore project on his personal YouTube, but only promotes his own products now.

We have always been wary of AI generated code, but felt everyone is free to do what they want and experiment, etc.

LLM tend to use certain font combos like Space Grotesk, Instrument Serif and Geist

I guess people will get back to crafting beautiful designs to stand out from the slop. On the other hand, I'm not sure how much design will still matter once AI agents are the primary users of the web.

A designer recently told me that 'colored left borders are almost as reliable a sign of AI-generated design as em-dashes for text'

The good world is where everyone has AI, and not as a revokable privilege through an API, but through hard possession.

It works for Mars. I think there's so much value in colonizing Mars, and it's sad to me to see SpaceX diluting the mission buying up random AI bubble crap.

How does a normal person fit into Elon's world? What institutions will Elon leave behind? Is there any value in that society to art and culture?

I can hear the rabid Elon fan defending him about Tesla patents or the Twitter algorithm or something, but those are not serious open source projects.

Even the ideal version, industrial megaprojects at hyperhuman scale while constantly being out over your skis with leverage sounds hellish.

Commoditizing complements doesn't always work because focus is scarce even for the largest, fastest growing businesses.

Some categories never developed a competitive response to this strategy : email, advertising infrastructure, user-generated video.

Several correlated but not strictly identical changes happened over the same few months: scaling inference compute, heavier use of RL in post-training, and models producing reasoning tokens.

Tasks where correctness is harder to verify may not have seen the same speedup, so the acceleration we document here may not be as general as the headline numbers suggest.

The three metrics where we find acceleration are concentrated in programming and mathematics. These are areas that labs have explicitly targeted for improvement, and they share an important property: correctness is easy to verify automatically.

Our fourth metric, an index constructed from WeirdML V2 results, showed no sign of acceleration. A single global linear trend fit the data best.

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Three of the four metrics (ECI, log METR 50% time horizon, and a math-focused index we constructed from several math benchmarks) show strong evidence that progress has sped up relative to a global linear trend fit to data from 2023 onward.

Within eight days, the same campaign had cascaded from GitHub Actions to Docker Hub, npm, PyPI, and the VS Code extension marketplace. With just one token across five ecosystems, thousands of organizations were potentially impacted.

Modern-day security tooling looks for the wrong things. Most software composition analysis tools work by checking your dependencies against a database of known vulnerabilities – CVEs. But a deliberately planted backdoor doesn't have a CVE.

The result is a mismatch that should terrify anyone building software: the attack surface is expanding faster than any human can monitor, and the entities making dependency decisions are increasingly not human.

Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve.

An AI researcher subsequently gifted them each a ChatGPT Pro subscription to encourage their 'vibe mathing.'

We have discovered a new way to think about large numbers and their anatomy. It's a nice achievement. I think the jury is still out on the long-term significance.

The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question.

Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve. He's 23 years old and has no advanced mathematics training.

Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve. He's 23 years old and has no advanced mathematics training.

What he does have is a ChatGPT Pro subscription, which gives him access to the latest large language models from OpenAI.

Lichtman tried to prove this, too, but got stuck like everyone else before him.

An AI researcher subsequently gifted them each a ChatGPT Pro subscription to encourage their 'vibe mathing.'

I had the intuition that these problems were kind of clustered together and they had some kind of unifying feel to them. And this new method is really confirming that intuition.