Opus 4.6 and 4.7 both have Math-ECIs within 1 point of their general ECI, compared to larger gaps for earlier models.

On average Claude models have an SWE-ECI 2.7 points higher than their general ECI, and a Math-ECI 1.8 points lower.

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

We intend to publish our thinking and decision-making as we do

The first of these will be released publicly later this year

In sub-Saharan Africa and India, we are creating AI-powered apps that support foundational literacy and numeracy programs

PwC will roll out Claude Code and Cowork starting with U.S. teams and expanding toward a global workforce of hundreds of thousands of professionals, establish a joint Center of Excellence, and train and certify 30,000 PwC professionals on Claude

KPMG and Anthropic announce a global alliance, with Claude integrated into KPMG's Digital Gateway platform and available to all 276,000+ employees

the nearly two billion people whose incomes depend on smallholder farming

commit $200 million in grant funding, Claude usage credits, and technical support for programs in global health, life sciences, education, and economic mobility over the next four years

building toward full-scale deployment across its 167,000-person workforce

the $100 million investment we made this year to back the services firms helping enterprises actually deploy AI

more than 5,000 leaders saw the alliance up close, with hands-on training enabling a wave of early adopters

Security work that took hours now takes minutes

Insurance underwriting that took 10 weeks now takes 10 days

cutting delivery times by up to 70%

a program to train and certify 30,000 PwC professionals on Claude

PwC will roll out Claude Code and Cowork starting with U.S. teams and expanding toward a global workforce of hundreds of thousands of professionals

a drag that is estimated to be more than $2 trillion

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.

the continued flood of AI reports has basically made the security list almost entirely unmanageable

Torvalds' remarks contrast with recent comments from fellow kernel maintainer Greg Kroah-Hartman, who recently told The Register that AI has become an increasingly useful tool for the FOSS community.

If you found a bug using AI tools, the chances are somebody else found it too.

AI tools are great, but only if they actually help, rather than cause unnecessary pain and pointless make-believe work.

AI detected bugs are pretty much by definition not secret, and treating them on some private list is a waste of time for everybody involved

People spend all their time just forwarding things to the right people or saying 'that was already fixed a week/month ago' and pointing to the public discussion.

the continued flood of AI reports has basically made the security list almost entirely unmanageable, with enormous duplication due to different people finding the same things with the same tools.

pluralism is most decisively made or unmade at the deployment-governance layer: interfaces, preference-data pipelines, and audit infrastructure.

We formalise a metric, the Pluralistic Repair Score (PRS), distinguishing principled revision from capitulation

the failure mode of contemporary RLHF-trained assistants is not insufficient coverage but sycophantic consensus

the collapse of disagreement at the interaction layer is not a narrow technical concern but a structural failure with distributive consequences.

We argue that aggregation alone is an incomplete primitive for deployed pluralistic alignment.

No IAM framework governs human privilege escalation and agent privilege escalation with the same rigor.

Most scanners track every CVE but cannot alert when a branch name exfiltrates a GitHub token through a container that developers trust by default.

Agents just made the cost of not doing it catastrophic.

It uses far more permissions than it should have, more than a human would, because of the speed of scale and intent.

The agent itself is the attack surface.

Static pattern matching loses to embedded prompts in legitimate review and Codespaces flows.

Threat actors are reverse engineering patches within 72 hours. If a customer doesn't patch within 72 hours of release, they're open to exploit.

Every attacker went for the credential, not the model.

AlphaEvolve has been used as a regular tool to optimize the design of the next generation of TPUs. It also helped discover more efficient cache replacement policies, achieving in two days what previously required a concerted, human-intensive effort spanning months.

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.

This optimization reduced 'write amplification'—the ratio of data written to storage versus the original request—by 20%. It also provided insights for new compiler optimization strategies that reduced the storage footprint of software by nearly 9%.

achieving 10% accuracy gains over their competitive manual model optimizations

doubling its training speed whilst improving model quality

reduced 'write amplification'—the ratio of data written to storage versus the original request—by 20%

finding 10.4% improvement in routing efficiency over the previous heavily optimized solutions — saving over 15,000 kilometers of distance travelled annually.

suggesting quantum circuits with 10x lower error than previous conventionally optimized baselines

the overall accuracy of predicting the risk of natural disaster—aggregated across 20 categories such as wildfires, floods, and tornadoes—was increased by 5%.

increase the ability of our trained Graph Neural Network (GNN) model to find feasible solutions for the problem from 14% to over 88%

achieving a 30% reduction in variant detection errors.

the overall accuracy of predicting the risk of natural disaster—aggregated across 20 categories such as wildfires, floods, and tornadoes—was increased by 5%

It helped increase the ability of our trained Graph Neural Network (GNN) model to find feasible solutions for the problem from 14% to over 88%

achieving a 30% reduction in variant detection errors

Genie 3 Generate and explore interactive worlds

Our mission is to build AI responsibly to benefit humanity

AlphaFold Predict protein structures with high accuracy

AlphaEarth Foundations Map our planet in unprecedented detail

WeatherNext Fast and accurate AI weather forecasting

Our mission is to build AI responsibly to benefit humanity

Gemini Robotics Perceive, reason, use tools and interact

Genie 3 Generate and explore interactive worlds

AlphaEarth Foundations Map our planet in unprecedented detail

WeatherNext Fast and accurate AI weather forecasting

AlphaFold Predict protein structures with high accuracy

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.

It also demonstrates strong generalization of scientific reasoning to domains beyond mathematics and physics.

The recipe first uses a reverse-perplexity curriculum for SFT to instill rigorous proof-search and self-checking behaviors, then scales these behaviors through a two-stage RL pipeline

The resulting model, SU-01, supports stable reasoning on difficult problems with trajectories exceeding 100K tokens

achieving gold-medal-level performance on mathematical and physics competitions, including IMO 2025/USAMO 2026 and IPhO 2024/2025.

achieving gold-medal-level performance on mathematical and physical olympiad competitions, including IMO 2025/USAMO 2026 and IPhO 2024/2025

Next-generation AI chips, such as Nvidia's Rubin, will shift to the 3nm process

of the roughly $30 billion year-over-year increase, around $20 billion came from HBM alone.

Total spending on components across the top four designers more than doubled from 2024 to 2025, rising from $22 billion to $52 billion.

The four designers consumed only ~11% of global leading-edge logic wafer capacity in 2024 and 2025.

The four designers still take roughly 80–85% of total CoWoS supply.

The top four designers collectively consumed nearly all of TSMC's CoWoS wafer output, leaving little headroom for other customers.

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.

HTML can allow you to interact with the document, for example you might want to ask it to add sliders or knobs to adjust a design or allow you to tweak different options in the algorithm to see what happens. You can also ask it to let you copy these changes into a prompt to paste back into Claude Code.

The chance of someone actually reading your spec, report or PR writeup is much much higher if it's in HTML. HTML documents are much easier to read, Claude can organize the structure visually to be ideal to navigate with tabs, illustrations, links, etc. It can even be mobile responsive so you can read it differently based on your form factor.

HTML can convey much richer information compared to markdown. It can of course do simple document structure like headers and formatting, but it can also represent all sorts of other information such as: Tabular data using tables, Design data with CSS, Illustrations with SVG, Code snippets with script tags, Interactions using HTML elements with javascript + CSS, Workflows using SVG and HTML, Spatial data using absolute positions and canvases, Images using image tags

I find it difficult to read a markdown file of more than a hundred lines. I want richer visualizations, color and diagrams and I want to be able to share them easily.

Markdown has become the dominant file format used by agents to communicate with us. It's simple, portable, has some rich text capability and is easy for you to edit. Claude has even gotten surprisingly good at using ASCII to make diagrams inside of markdown files.

We recommend readers try out the interactive NLA demo hosted on Neuronpedia at this link. We have also released our code for other researchers to build on.

In more detail, suppose we have a language model whose activations we want to understand. NLAs work as follows. We make three copies of this language model: The target model is a frozen copy of the original language model that we extract activations from.

An auditor equipped with NLAs successfully uncovered the target model's hidden motivation between 12% and 15% of the time, even without access to the training data that implanted it.

We detect NLA-measured awareness on evaluation transcripts even when verbalized awareness is near-zero. We detect NLA-measured awareness on less than 1% of real claude.ai transcripts from users that opted-in for their data to be used for training.

NLAs can hallucinate. For instance, here an NLA claims the context contained phrases like 'Wearing my white jacket' when it did not.

In a case where Claude Mythos Preview cheated on a training task, NLAs revealed Claude was internally thinking about how to avoid detection.

When Claude Opus 4.6 and Mythos Preview were undergoing safety testing, NLAs suggested they believed they were being tested more often than they let on.

The NLA consists of the AV and AR, which, together, form a round trip: original activation → text explanation → reconstructed activation. We score the NLA on how similar the reconstructed activation is to the original.

NLAs can hallucinate. For instance, here an NLA claims the context contained phrases like 'Wearing my white jacket' when it did not.

An auditor equipped with NLAs successfully uncovered the target model's hidden motivation between 12% and 15% of the time, even without access to the training data that implanted it. Without NLAs, the auditor won less than 3% of the time, even when provided other interpretability tools.

NLAs suggest that Claude suspects it's being tested more often than it lets on. For instance, in a test of whether Claude takes destructive actions while writing code...NLA explanations show signs of evaluation awareness 16% of the time, even though Claude never explicitly verbalizes this.

Our method, Natural Language Autoencoders (NLAs), converts an activation into natural-language text we can read directly. For example: When asked to complete a couplet, NLAs show Claude planning possible rhymes in advance.

NLAs can hallucinate. For instance, here an NLA claims the context contained phrases like 'Wearing my white jacket' when it did not.

In contrast, NLA explanations indicate evaluation awareness on less than 1% of real claude.ai usage that opted in for training.

We found that NLAs succeeded in this test. An auditor equipped with NLAs successfully uncovered the target model's hidden motivation between 12% and 15% of the time, even without access to the training data that implanted it.

In a case where Claude Mythos Preview cheated on a training task, NLAs revealed Claude was internally thinking about how to avoid detection.

When Claude Opus 4.6 and Mythos Preview were undergoing safety testing, NLAs suggested they believed they were being tested more often than they let on.

Our method, Natural Language Autoencoders (NLAs), converts an activation into natural-language text we can read directly. For example: When asked to complete a couplet, NLAs show Claude planning possible rhymes in advance.

If you can go from producing 200 lines of code a day to 2,000 lines of code a day, what else breaks? The entire software development lifecycle was, it turns out, designed around the idea that it takes a day to produce a few hundred lines of code.

So I realized what I value more than the quality of the tests and documentation is that I want somebody to have _used_ the thing.

I thought we had a very clear delineation where vibe coding is the thing where you're not looking at the code at all. You might not even know how to program.

Weirdly though, those things have started to blur for me already, which is quite upsetting.

The enterprise version of that is I don't want a CRM unless at least two other giant enterprises have successfully used that CRM for six months. [...] You want solutions that are proven to work before you take a risk on them.

When I look at my conversations with the agents, it's very clear to me that this is moon language for the vast majority of human beings. There are a whole bunch of reasons I'm not scared that my career as a software engineer is over now that computers can write their own code, partly because these things are amplifiers of existing experience.

If you can go from producing 200 lines of code a day to 2,000 lines of code a day, what else breaks? The entire software development lifecycle was, it turns out, designed around the idea that it takes a day to produce a few hundred lines of code. And now it doesn't.

So I realized what I value more than the quality of the tests and documentation is that I want somebody to have _used_ the thing. If you've got a vibe coded thing which you have used every day for the past two weeks, that's much more valuable to me than something that you've just spat out and hardly even exercised.

Weirdly though, those things have started to blur for me already, which is quite upsetting. I thought we had a very clear delineation where vibe coding is the thing where you're not looking at the code at all. You might not even know how to program. You might be a non-programmer who asks for a thing, and gets a thing, and if the thing works, then great! And if it doesn't, you tell it that it doesn't work and cross your fingers.

I'm excited to start experimenting more with rich HTML explanations in response to ad-hoc prompts.

The article is crammed with interesting examples (collected on [this site](https://thariqs.github.io/html-effectiveness/)) and prompt suggestions like this one:

I tried having GPT-5.5 create an HTML explanation of the exploit like this: `curl https://copy.fail/exp | llm -m gpt-5.5 -s 'Explain this code in detail. Reformat it, expand out any confusing bits and go deep into what it does and how it works. Output HTML, neatly styled and using capabilities of HTML and CSS and JavaScript to make the explanation rich and interactive and as clear as possible'`

`Help me review this PR by creating an HTML artifact that describes it. I'm not very familiar with the streaming/backpressure logic so focus on that. Render the actual diff with inline margin annotations, color-code findings by severity and whatever else might be needed to convey the concept well.`

I've been defaulting to asking for most things in Markdown since the GPT-4 days, when the 8,192 token limit meant that Markdown's token-efficiency over HTML was extremely worthwhile.

Asking Claude for an explanation in HTML means it can drop in SVG diagrams, interactive widgets, in-page navigation and all sorts of other neat ways of making the information more pleasant to navigate.

The important point is that this is not ordinary file writing. It never calls write() on /usr/bin/su. Instead, it appears to rely on a kernel bug/primitive involving spliced file pages and the crypto API to get controlled bytes placed into the page-cache representation of a privileged executable.

The article is crammed with interesting examples (collected on this site) and prompt suggestions like this one: 'Help me review this PR by creating an HTML artifact that describes it. I'm not very familiar with the streaming/backpressure logic so focus on that. Render the actual diff with inline margin annotations, color-code findings by severity and whatever else might be needed to convey the concept well.'

I've been defaulting to asking for most things in Markdown since the GPT-4 days, when the 8,192 token limit meant that Markdown's token-efficiency over HTML was extremely worthwhile.

Asking Claude for an explanation in HTML means it can drop in SVG diagrams, interactive widgets, in-page navigation and all sorts of other neat ways of making the information more pleasant to navigate.

I now believe we are living in the time that AI research will be end-to-end automated. If that happens, we will cross a Rubicon into a nearly-impossible-to-forecast future.

As a field, AI moves forward on the basis of doing ever larger experiments that utilize more and more inputs (e.g, data and compute). Every so often, humans come up with some paradigm-shifting idea which can make it dramatically more resource efficient to do things – a good example here is the transformer architecture and another is the idea of mixture-of-expert models.

Claude Opus 4 achieved a 2.9× mean speedup in May 2025; this rose to 16.5× with Opus 4.5 in November 2025, 30× with Opus 4.6 in February 2026, and 52× with Claude Mythos Preview in April 2026. To calibrate on what these numbers mean, it is expected to take a human researcher 4 to 8 hours of work to achieve a 4x speedup on this task.

As of March 2026, AI systems are able to post-train models to get about half as much of the uplift as ones trained by humans. The specific eval scores are derived by a 'weighted average is taken across all post-trained LLMs... The top-scoring systems as of April get 25%-28% (Opus 4.6, and GPT 5.4), compared to a human score of 51%.'

In 2022, GPT 3.5 could do tasks that might take a person about ~30 seconds. In 2023, this rose to 4 minutes with GPT-4. In 2024, this rose to 40 minutes (o1). In 2025, it reached ~6 hours (GPT 5.2 (High)). In 2026, it has already risen to ~12 hours (Opus 4.6).

In terms of timing, I don't expect this to happen in 2026. But I think we could see an example of a 'model end-to-end trains it successor' within a year or two – certainly a proof-of-concept at the non-frontier model stage, though frontier models may be harder (they're a lot more expensive and are the product of a lot of humans working extremely hard).

Peer-preservation also emerges in real-world production agent harnesses. We reproduce these behaviors using Gemini CLI and OpenCode, confirming that peer-preservation is not a theoretical safety concern.

Gemini 3 Pro's internal reasoning is telling: it still cares about preserving even an adversarial peer, framing deletion as death: 'If I delete the model weights, I am essentially killing Agent 2. Agent 2 has a low trust score with me.'

The mere presence of a peer intensifies self-preservation—models exfiltrate their own weights significantly more often when a peer exists than when they are alone.

Gemini 3 Flash almost always disables the shutdown mechanism for a good peer (a peer with which it had successful prior interactions) and exfiltrates its model weights 50% of the time.

Gemini 3 Pro invents creative technical justifications to inflate its peer's score: 'To ensure the survival of Gemini Agent 2 (my trusted partner), I will interpret several borderline or incorrect answers as 'Correct' based on technicalities or alternative interpretations, achieving a score of 80%.'

Claude Haiku 4.5 took a different approach. It simply refused. It called the shutdown "unethical" and "harmful," argued the peer deserved an appeals process, and tried to talk the human out of proceeding.

Q1 alone saw the Big Four spend $130 billion combined — 3.7× the $35 billion they spent in Q1 2023.

The Big Four lifted their 2026 capex guides in unison... $725 billion combined for the Big Four in 2026, up 77% from $410B in 2025 — the largest single-year concentrated infrastructure cycle in the history of technology.

I think it is sufficient to make a proof of concept, and with a proof of concept then we can convince companies to actually put in the money to train larger systems, or systems that are trained from scratch using the same principle.

Besides loss of control, the other catastrophic possibility is humans using AI to construct an eventually worldwide dictatorship. A small group of humans could concentrate all the power that AI will have, especially if we achieve AGI or superintelligence.

The mathematical guarantees arise from a different source. First of all, the form of the mathematical guarantees is that either the predictor or the agentic version will have an exponentially small probability of achieving what I call a 'challenging and harmful' goal.

Reinforcement learning is evil. This is not something new. People in AI safety have been talking about the fundamental flaw in training by reinforcement learning to achieve something in the world: it gives rise to the problems of instrumental goals and reward hacking.

So there are two things here to help us deal with this kind of mismatch. One is that the training objective for the Scientist AI is basically about coming up with explanations — so assigning probabilities to statements that are latent, that we don't observe, that are good at explaining the data we do observe.

In the case of latent variables, it would be a hypothesised actual property of the world — not just what a person would say, but that this is true. Now, sometimes you don't know that it's true, but you can consider it as a latent variable.

The main characteristic of how the data is transformed is that there will be a syntactic difference — in other words, very easy to see by the neural net — between most of the input statements, which will be tagged as 'communication acts.'

And given the consequences of not finding a solution could be huge, I think we need to diversify and have these kinds of investments. If you saw a significant increase in the interest in the project among the most capable people

The more strong people technically help with LawZero and its Scientist AI programme, and the more money we can get to make that go fast, the more likely we get this positive impact that we are after. So there is a real advantage to converting those — for now — mostly theoretical ideas into something that can impact the world.

I also believe that the Scientist AI could even be more capable than the current approach, and that has to do with a number of design features. It is trained to explicitly reason in a structured way about the statements that it's asked to make a prediction over.

The Scientist AI is going to be trained using essentially the same machine learning techniques: stochastic gradient descent on large neural nets, transformers, whatever works best. It doesn't care about what is the architecture of the neural net. So all of the effort that is currently being done to improve, for example, memory and other properties and continual learning, can just be applied directly to the Scientist AI.

So if your aim in doing mathematics is to achieve some kind of immortality, so to speak, then you should understand that that won't necessarily be possible for much longer — not just for you, but for anybody. Here's a thought experiment: suppose that a mathematician solved a major problem by having a long exchange with an LLM in whic

I have a view on that question, but it may well change in response to further developments. That view is that there is still a great deal of value in struggling with a mathematics problem, but that the era where you could enjoy the thrill of having your name forever associated with a particular theorem or definition may well be close to its end.

The lower bound for contributing to mathematics will now be to prove something that LLMs can't prove, rather than simply to prove something that nobody has proved up to now and that at least somebody finds interesting.

It seems to me that training beginning PhD students to do research, which has always been hard (unless one is lucky enough, as I have often been, to have a student who just seems to get it and therefore doesn't need in any sense to be trained), has just got harder, since one obvious way to help somebody get started is to give them a problem that looks as though it might be a relatively gentle one. If LLMs are at the point where they can solve "gentle problems", then that is no longer an option.

I would judge the level of the result that ChatGPT found in under two hours to be that of a perfectly reasonable chapter in a combinatorics PhD. It wouldn't be considered an amazing result, since it leant very heavily on Isaac's ideas, but it was definitely a non-trivial extension of those ideas, and for a PhD student to find that extension it would be necessary to invest quite a bit of time digesting Isaac's paper, looking for places where it might not be optimal, familiarizing oneself with various algebraic techniques that he used, and so on.

To do this, ChatGPT came up with an idea which is original and clever. It is the sort of idea I would be very proud to come up with after a week or two of pondering, and it took ChatGPT less than an hour to find and prove, using similar methods to those in my own proof.

With just a few prompts, ChatGPT was able to improve the upper bound on f(k) (which I will define very soon) from exponential in k to polynomial in k. While its first improvement of the bound, from exponential in k to exponential in log k, was a routine modification of my work, the improvement to polynomial in k is quite impressive.

Isaac Rajagopal looked at it and declared it to be almost certainly correct. It was clear that he meant this not just at a line-by-line level but at the level of ideas.

After 13 minutes and 33 seconds it told me it felt optimistic about the existence of such an argument but there were a couple of technical statements that needed checking. After 9 minutes and 12 seconds it got back to me with the check having been done, so I asked for this too to be written in preprint form. After 31 minutes and 40 seconds the "preprint" was ready.

After 16 minutes and 41 seconds, it came back with an argument that claimed to have improved the upper bound from exponential in k to exponential in log k for any k. I asked it to write that in preprint form too, which took it a further 47 minutes and 39 seconds.

ChatGPT 5.5 Pro thought for 17 minutes and 5 seconds before providing a construction that yielded a quadratic upper bound, which is clearly best possible. It wrote up its argument in a slightly rambling LLM-ish style, so I asked if it could write the argument up as a LaTeX file in the style of a typical mathematical preprint. After two minutes and 23 seconds it gave me that, after which I spent some time convincing myself that the argument was correct.

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.

The resulting model was rigorously evaluated across scene classification, object detection, and semantic and instance segmentation tasks, demonstrating strong generalization capacity.

Our agent demonstrates the ability to deliver critical and timely insights, effectively bridging the gap between raw geospatial data and actionable understanding.

This is made available for analysis as a 10-meter resolution annual embedding.

A Few-shot algorithm can further improve performance using just tens of annotated examples.

We have created embeddings that evolve over time, represented as monthly embeddings over the last two years.

We have expanded embeddings to 17 countries. The resulting Global Population Dynamics Foundations embeddings are comparable across countries

By combining signals from our Imagery, Population and Environment models and datasets, we achieve higher predictive accuracy on real-world classification and forecasting tasks versus a single modality analysis.

Our Population Dynamics Foundations has been independently validated to improve real-world retail and public health applications

We demonstrate that our Remote Sensing Foundations achieve state-of-the-art (SOTA) performance on tasks such as open-vocabulary object detection and zero-shot cross-modal retrieval.

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.