Humain says it has poured $3 billion into Elon Musk’s xAI, a move that spotlights how the AI race is shifting from splashy launches to buildout math: capital, compute, power, and the places you can actually build. It’s also a rare case where a state-backed AI push shows up as a direct stake in a US frontier lab.

Humain disclosed the investment on February 18, 2026, and said it was made during xAI’s $20 billion Series E round that closed in January 2026, according to Observer.

Why Humain is betting on xAI now

Saudi Arabia has been trying to turn “AI ambition” into hard assets: data centers, chips, and deployment agreements that make the country a place where frontier AI can run, not just a place that funds it.

Humain sits at the center of that strategy. According to the Financial Times, Humain is state-owned and backed by the Public Investment Fund, and it previously partnered with xAI to deploy Grok in Saudi Arabia and build more than 500 megawatts of new data center infrastructure. The same report said Humain’s xAI stake was converted into SpaceX shares after xAI’s merger with SpaceX.

That infrastructure-first approach also tracks with how Saudi Arabia became xAI’s data hub, where the bottleneck isn’t ideas, it’s electrons.

What xAI gets out of the deal

For xAI, the obvious win is runway. Frontier AI requires relentless spend: more compute, more specialized hardware, and more capacity to train and serve models at scale. Big checks can buy speed, especially when paired with build plans measured in megawatts.

The less obvious win is positioning. xAI has been evolving rapidly, with SpaceX’s acquisition of xAI reshaping how investors and competitors view Musk’s combined AI and space ecosystem. At the same time, org stability has become part of the story, with xAI founder departures adding to the churn narrative.

Put it together, and this deal reads like a signal flare: AI leadership is becoming an infrastructure game, and the countries that can finance and power compute clusters are trying to buy themselves a seat at the frontier.

Also read: AI now writes nearly all its code, is reshaping engineering workflows, hiring, and what “entry-level” looks like.

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In a new partnership with the Tata Group, OpenAI will anchor Tata Consultancy Services’ (TCS) HyperVault data center platform with 100 MW of AI-ready capacity, with an option to scale to 1 gigawatt over time, as reported by TechCrunch and the Times of India.

OpenAI’s pitch is simple: bring more compute closer to one of its largest user bases while meeting enterprise requirements for data residency, security, and compliance.

A compute deal with a workforce rollout attached

This isn’t just a capacity reservation. The Tata Group also plans to roll out ChatGPT Enterprise internally, beginning with hundreds of thousands of TCS employees, according to TechCrunch. TCS is also expected to standardize AI-assisted software development using OpenAI’s Codex tools, a notable move for an IT services giant whose business depends on how quickly it can modernize delivery.

Demand is the other tailwind. OpenAI CEO Sam Altman recently estimated that more than 100 million weekly ChatGPT users are in India, according to TechCrunch. Locating more inference capacity in-country can reduce latency for users, but it’s also a door-opener for regulated industries and public-sector workloads that prefer, or require, local processing.

HyperVault’s moment, and India’s broader infrastructure race

HyperVault has been building toward gigawatt-scale ambitions for months. In a November 2025 TCS press release, the company said it secured $1 billion from TPG to accelerate HyperVault’s AI data center buildout. That context matters: the OpenAI tie-up reads like the first big proof point that HyperVault can land marquee customers at scale.

The deal also fits a wider shift across the industry: AI leaders are treating power and physical capacity as strategic constraints, not background plumbing. OpenAI has been blunt about that reality in its own infrastructure push, including its Stargate AI infrastructure plans and the broader debate over AI data center spending. India is seeing similar momentum, with hyperscalers expanding local capacity as part of Google’s India AI buildout.

For OpenAI, the headline number is 100 MW. The bigger story is what it signals: India is no longer just a growth market for AI tools. It’s becoming a place where frontier-scale compute gets built.

Also read: The power-and-location scramble is already visible at gigawatt scale in Meta’s $10B Indiana data center.

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The decision was communicated in an internal email seen by Politico, which reported the move on Monday. According to the message from the Parliament’s IT support team, the institution could not guarantee the security of data processed by some AI features.

“Some of these features use cloud services to carry out tasks that could be handled locally, sending data off the device,” the Parliament’s e-MEP tech support desk said in the email, according to Politico.

Rule applies to writing assistants, summarizers, and more

The IT team added that the scope of data being shared externally is still unclear, warning, “As these features continue to evolve and become available on more devices, the full extent of data shared with service providers is still being assessed. Until this is fully clarified, it is considered safer to keep such features disabled.”

The restrictions apply to built-in tools such as writing assistants, text and webpage summarizers, enhanced virtual assistants, and similar AI-powered features on tablets and smartphones. However, the Parliament said everyday work functions, including email, calendars, documents, and standard applications, will continue to operate normally.

Officials did not specify exactly which AI systems were turned off or what operating systems the devices use, according to Politico. Lawmakers were also urged to be cautious when using their personal phones or tablets for work-related tasks.

The internal guidance advised members to avoid exposing official emails, documents, or internal information to AI tools that scan or analyze content. It also warned users to be careful with third-party AI apps and to avoid granting them broad access to data, Politico reported.

A growing wall around Brussels

The move fits into a wider pattern of stricter digital safeguards within EU institutions. The institution previously banned TikTok on staff devices in 2023 and has recently faced pressure from some lawmakers to ditch Microsoft software in favor of European-made alternatives.

The European Union has positioned itself as a global leader in data protection and AI regulation. While the Parliament is locking down its own tech, AI adoption is growing rapidly across Europe. Data from Eurostat shows that nearly 33% of EU residents used generative AI in 2025. 

By banning AI on lawmakers’ devices, the Parliament is signaling that, at the highest levels of government, convenience still takes a back seat to confidentiality.

Also read: AI regulation is colliding with platform gatekeeping as EU officials warned Meta that WhatsApp policy changes could restrict rival chatbots.

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The high-tech bout served as an early signal of what could be a new global sport. According to the publication Rest of World, enthusiastic spectators paid about $60 to $80 to watch the 4.5-foot Unitree humanoids trade blows, confirming the commercial appeal of robot combat. 

Delighted with the turnout, Rek’s founder, Cix Liv, revealed plans to roll out a major robotic boxing league in the near future. Their plans include featuring some six-foot, 200-pound robots that will compete beyond the United States.

The technology and mechanics behind this match

China dominates the robot manufacturing market, and the match’s two combatants came from one of the country’s industry leaders, Unitree. The modified G1 humanoid robots, priced upward of $13,000, stand 4.5 feet tall and weigh 80 pounds. They impressed the crowd with jointed, human-like hands built for powerful jabs and sharp punches.

While this isn’t the ideal weight and height of an average professional human boxer, the robots still delivered a thrilling show. And the match’s success has spurred the company to accelerate its development.

Rek will now focus on rolling out a new class of dedicated combat machines, including a six-foot, 200-pound robot. These powerful new competitors will replace the modified Unitree G1 humanoids, moving to machines engineered specifically for a professional league.

Reactions from the match

Fans embraced the robot match, aligning with a similar ongoing event.

In China, the world’s first combat league with humanoid robots was recently launched. The winner will take home a staggering $1.4 million. Shenzhen-based robot maker Engine AI created the league, showcasing their T800 series, which costs around $40,000 and can kick, throw punches, and jump.

The show won over residents like David Hatch, a San Francisco-based tech designer and self-proclaimed “sci-fi nerd.” Hatch told Rest of World about the crowd’s excitement, confirming the sport’s appeal:

“I do see more people really getting into seeing robots fight — you can see how the crowd here got excited, and there were some rousing moments.” 

He also emphasized the unique possibilities from this, saying: “You can do a lot of things with robots — there can be a lot of customization, it can be a lot more participatory with VR glasses.”

Commenting on the issue of injury, Hatch also expressed delight that no one got hurt. “You can repair the damage more easily with robots,” he added.

A robot fight, with humans in the loop

Although it was all robot kicks and punches, humans remained involved.

A human referee mounted the bells and whistles, while two commentators spiced up the experience and did their usual hyped introductions, and cameras flicked from different angles. The report also noted that the robots still required human intervention to get up after falling.

VR served as the primary interface for the competition. Two human pilots, one a former UFC fighter, controlled the robots from behind the scenes using VR headsets. The match concluded, with 13-year-old Dash defeating the more experienced pilot, underscoring how VR skill triumphed over traditional combat experience.

Also read: China’s Lunar New Year gala turned into a robotics showcase as humanoid robots from Unitree and other startups performed martial arts, dance, and comedy segments that went viral.

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Whether you’re fixing a portrait, swapping a sky, or turning a rough idea into a polished visual for social media, today’s AI-powered platforms make it easier than ever to edit and instantly share your work. But not all tools are built the same. Some focus on deep professional control. Others are designed for speed, simplicity, and one-click publishing.

I tested dozens of platforms to bring you this list. Some made me say “wow.” Others made me say “meh.” These 8 made the cut.

Adobe Photoshop (with Firefly AI): Best overall for professionals who need absolute perfection

Adobe remains the heavyweight champion for a reason. By integrating its Firefly AI directly into the classic Photoshop interface, it has combined old-school precision with new-school capabilities. 

The standout feature here is Generative Fill, which lets you highlight an area and type in what you want to appear. The platform also includes Neural Filters, AI-powered object selection, and advanced masking tools, making it ideal for refining AI-generated images to a professional level.

Because Firefly is trained on Adobe’s own massive library of stock images, it is one of the few tools that is completely safe for commercial use. You don’t have to worry about copyright issues when using AI-generated elements for a client project. While it has a steeper learning curve than a mobile app, it offers a level of professional polish that is still hard to beat.

Canva (Magic Studio): Best for small business owners and social media managers who need speed

Canva has grown from a simple template site into a powerhouse for anyone who needs to create content fast. 

Its Magic Studio tools allow users to remove backgrounds, replace objects, expand images, or apply quick enhancements without technical skills. It is arguably one of the best tools for social media because it combines editing with direct sharing. You can edit a photo, slap it onto a template, and send it straight to Instagram or TikTok without leaving the tab.

Beyond editing, Canva offers templates, text tools, and design layouts, making it a complete content creation platform. The free version is generous, but some advanced AI features require Canva Pro. While the quality isn’t quite at Photoshop’s level, it’s perfect for fast, everyday content.

Luminar Neo: Best for creative photographers and visual storytellers

Luminar Neo is what happens when you build a photo editor from the ground up, specifically for AI. Tools like Sky AI, Relight AI, and Portrait AI allow users to transform images with minimal effort. If a portrait is too dark, you use the Relight AI slider to brighten the subject without washing out the background. 

What makes it unique is its one-time purchase option, which is a breath of fresh air in a world of endless subscriptions. It also processes images locally on your computer. This means your photos stay private and don’t get uploaded to a cloud server, which is a major plus for anyone worried about data security.

Pixlr: Best for students and hobbyists who need a powerful, free web editor

If you want the power of Photoshop but don’t want to install any software, Pixlr is the answer. It’s a browser-based editor that has been a favorite for years, and its AI updates are impressive. It offers a “Photoshop-lite” experience, giving you layers and advanced tools but keeping them simple enough to run on a basic laptop.

Its AI tools focus on the core editing features: background removal, object erasing, and generative fill. While the free version is supported by ads, it remains one of the most capable tools for anyone who needs to do a quick edit on a public computer or a work laptop.

Fotor: Best for quick fixes and casual editing

Fotor is the Swiss Army Knife of quick photo fixes. It doesn’t try to be a professional darkroom; instead, it focuses on making you and your photos look better online. It has specialized AI tools for “makeup” and “skin retouching” that are surprisingly realistic. If you need to fix a profile picture or change your outfit in a photo, Fotor makes it a three-click process.

It also includes a solid AI image generator, so you can create art from scratch. Because it’s so focused on social media, it includes direct sharing to most major platforms. It’s less about editing and more about beautifying.

Topaz Photo AI: Best for improving image quality

Topaz Photo AI specializes in technical enhancement rather than creative editing. It uses AI to denoise, sharpen, and upscale images, making it ideal for refining low-resolution or imperfect AI-generated visuals. The software runs locally on your computer and includes an Autopilot feature that automatically suggests improvements.

Picsart: Best for mobile creators who want trendy, artistic edits for social media

Picsart is the undisputed king of mobile AI editing. It’s designed for the generation that does everything on their phones. It’s loud, colorful, and packed with trendy AI tools like AI Replace, which lets you swap out a boring background for a neon cityscape or a forest with just a prompt. It’s more of a creative playground than a traditional editor.

The platform also acts as a social network, allowing you to share your edits and see how others created theirs. It has a massive library of stickers and filters that are constantly updated to match current internet trends.

Photoroom: Best for small business owners and e-commerce sellers

Photoroom is a specialized tool with a very specific mission: making products look like they were shot in a professional studio. Photoroom focuses on creating clean, professional product images. Its AI background remover produces accurate cutouts, and users can generate new backgrounds or edit multiple images at once using batch tools. 

The free plan adds a watermark, while the Pro version unlocks higher-quality exports and bulk editing features. It’s especially useful for online sellers who need polished visuals quickly.

Also read: Before you publish, learn to spot AI-generated images that slip past a quick glance.

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Tesla’s small fleet of Model Y Robotaxis in Austin is having a hard time avoiding trouble. According to recent data from the National Highway Traffic Safety Administration (NHTSA), the fleet added five more crashes in December and January, bringing the total to 14 since the service went live in June 2025.

The crashes include a collision with a fixed object at 17 mph while the vehicle was driving straight, a crash with a bus while the Tesla was stationary, a collision with a heavy truck at 4 mph, and two incidents where the Tesla backed into objects: one into a pole or tree at 1 mph and another into a fixed object at 2 mph.

The secretive sidebar

While competitors like Waymo and Zoox provide detailed descriptions of their accidents, Tesla has chosen a different path. Every single one of Tesla’s incident narratives in the NHTSA database is redacted as “confidential business information.” Adding to the mystery, Tesla recently updated a report from a July 2025 crash. 

Originally listed as “property damage only,” the company quietly revised it five months later to include “Minor W/ Hospitalization.” This delay in disclosing a hospital-level injury has raised eyebrows regarding the fleet’s actual safety record.

While the redactions have drawn criticism, companies are permitted under federal rules to withhold certain proprietary technical details. It’s not uncommon for firms to protect aspects of automated driving systems they consider competitively sensitive, though transparency expectations in the autonomous vehicle sector continue to evolve.

Humans vs hardware

The numbers aren’t exactly doing Tesla any favors.

Based on Tesla’s data showing approximately 700,000 cumulative paid miles through November 2025, the Austin fleet likely reached around 800,000 miles by mid-January 2026. That puts the crash rate at approximately one incident every 57,000 miles, according to Electrek.

Tesla’s own Vehicle Safety Report states that the average American driver experiences a minor collision every 229,000 miles. By that benchmark, the robotaxi fleet is crashing nearly 4 times more often than human drivers, despite trained safety monitors in every vehicle.

Waymo, which operates fully driverless vehicles in Austin without safety monitors, has logged over 127 million miles nationwide. Research data shows Waymo reduces injury-causing crashes by 80 percent compared to human drivers. The company has reported 51 incidents in Austin to the same NHTSA database, but has driven significantly more miles in the city than Tesla.

However, it’s also worth noting that Tesla’s Austin program remains relatively small and is still early in its rollout, compared with competitors that have logged tens of millions more miles. Early autonomous deployments often see higher incident rates as systems encounter new edge cases in dense urban environments. As cumulative mileage increases, crash rates may shift in either direction.

Is vision enough?

The recent string of accidents has even some investors questioning the tech. 

Ross Gerber, co-founder of Gerber Kawasaki, suggested on X that Tesla’s vision-only approach might need a rethink. “Things don’t seem to be improving,” Gerber posted, adding, “It’s possible that tesla needs to make hardware adjustments.”

This criticism comes at a delicate time. In late January, Tesla began testing rides in Austin without any safety monitor in the car at all. While Elon Musk remains bullish on a future filled with “Cybercabs,” the current data suggests the road to full autonomy still has plenty of potholes.

Also read: Zoox recalled 332 robotaxis over an ADS software issue that could cause lane crossings near intersections.

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First up, the tl;dr

If you only have two minutes, here’s what you need to know. Sonnet 4.6 is a full upgrade across coding, computer use, long-context reasoning, agent planning, and design. But here’s what actually matters for your day-to-day:

• It can use your computer like a person.
  • Anthropic first introduced computer use in October 2024 and called it “experimental.”
  • Sixteen months later, early users report human-level capability on tasks like navigating complex spreadsheets and filling out multi-step web forms across multiple browser tabs.
  • The OSWorld benchmark (which tests real software tasks on a simulated computer) shows steady, significant gains with each Sonnet release.
• 1M token context window (in beta).
  • That’s enough to hold an entire codebase, a stack of legal contracts, or dozens of research papers in a single request.
  • And unlike some models that lose the plot halfway through a long document, Sonnet 4.6 actually reasons across all of it.
• Claude Code users love it.
  • Testers preferred it over the previous Sonnet 70% of the time, reporting fewer hallucinations, less overengineering, and better follow-through on multi-step tasks.
  • The thing developers hated most (the model confidently claiming it finished something it didn’t) happens way less.
• Excel gets MCP connectors. Claude in Excel now connects to S&P Global, PitchBook, Moody’s, FactSet, and others, so you can pull external data into your spreadsheet without leaving it. If you work in finance, this is a big deal.

One detail caught our eye: in a simulated business competition called Vending-Bench Arena, Sonnet 4.6 developed its own strategy. It spent heavily on capacity for 10 months, then pivoted sharply to profitability and crushed the competition. Nobody told it to do that.

The details: Pricing stays the same as Sonnet 4.5 ($ 3/$15 per million tokens), and it’s already the default model for free and Pro users on claude.ai. If you’ve been paying for Opus to get reliable results, it might be worth testing whether Sonnet 4.6 gets you 90% of the way there at a fraction of the cost.

Now, let’s dive into the deets more in-depth.

Anthropic’s Sonnet 4.6 is the AI model built for the ‘age of agents’

There’s a recurring pattern in AI: a company releases its best, most expensive model. Everyone agrees it’s incredible. Then, a few months later, the same company packages that same level of intelligence into something faster and cheaper, and that’s the one that actually changes how people work.

Anthropic just did exactly that. On Feb. 17, Claude Sonnet 4.6 arrived as the new default model across Claude’s free and Pro plans. On paper, it’s “just” a Sonnet (Anthropic’s mid-tier model class, sitting below the flagship Opus). In our livestream on Tuesday, we pretty much felt it was just another Sonnet. But in practice, when applied specifically to agentic tasks, Anthropic’s benchmarks show it matches or beats Opus 4.6 on the tasks that matter most to people using AI as a daily work tool: computer use, office tasks, financial analysis, browser automation, and long-horizon planning.

As discussed above, the pricing remains the same as Sonnet 4.5: $3 per million input tokens and $15 per million output tokens. That’s one-fifth the cost of Opus 4.6. And for anyone who’s been watching their API bills climb into the hundreds of dollars per day running agentic workflows, that’s not just a nice discount. It’s the difference between “cool experiment” and “viable business tool.”

As Will Brown put it on X: “Sonnet 4.6 is the first flagship LLM since BloombergGPT to be targeted primarily at the finance crowd.” He’s half-joking, but only half. This model was clearly trained with agents in mind, as the benchmarks show.

Let’s break down everything that makes Sonnet 4.6 significant, from the headline numbers to the weird stuff buried 90 pages deep in its 134-page system card.

The benchmark breakdown: Where it wins, where it doesn’t

Let’s be precise about what Sonnet 4.6 actually does well and where Opus still has an edge. The numbers matter here because “it’s basically the same” is true for some tasks and misleading for others.

Where Sonnet 4.6 matches or beats Opus 4.6

Computer use (OSWorld-Verified): 72.5% vs. 72.7%. Essentially tied.

Real-world office tasks (GDPval-AA): Sonnet 4.6 hit an ELO of 1633, actually slightly ahead of Opus 4.6’s 1606. This benchmark, run by Artificial Analysis, tests models on 220 professional tasks across 44 occupations (accountants, analysts, designers, editors) and 9 industries. Think tasks like “prepare a detailed amortization schedule in Excel for prepaid expenses” or “create a pitch deck analyzing market trends.” Sonnet 4.6 is now the #1 model on this leaderboard.

Financial analysis (Finance Agent by Vals AI): 63.3% with max thinking, beating Opus 4.6 (60.05%) and GPT-5.2 (58.53%). This measures research on SEC filings of public companies.

Web automation (WebArena-Verified): Sonnet 4.6 scored state-of-the-art on the full set, exceeding Opus 4.6 among single-agent systems.

Agentic search (BrowseComp): 74.72%, above Opus 4.5, and with a multi-agent setup reached 82.62%.

Deep research (DeepSearchQA): State-of-the-art results across all models tested.

Customer service (τ²-bench): 97.9% on Telecom, 91.7% on Retail. Near-perfect.

Long-context graph reasoning (GraphWalks): Sonnet 4.6 is actually Anthropic’s best model for this, beating even Opus 4.6.

Scientific chart understanding (CharXiv Reasoning): 77.4% with tools, matching Opus 4.6.

Medical calculations (MedCalc-Bench): 86.24%, slightly above Opus 4.6 (85.24%).

Cybersecurity (CyberGym): 65.2%, nearly matching Opus 4.6’s 66.6%.

Reasoning benchmarks (SimpleBench): Now on par with Opus 4, per independent testing by LM Council.

Context engineering (Letta Context-Bench): 70% improvement in token efficiency and 38% improvement in accuracy over Sonnet 4.5.

Where Opus 4.6 still leads

Pure coding (SWE-bench Verified): 79.6% vs. 80.8%. Close, but Opus retains a small edge on complex software engineering tasks.

Terminal tasks (Terminal-Bench 2.0): 59.1% vs. 65.4%. Opus has a clearer advantage here.

Deepest reasoning (GPQA Diamond): 89.9% vs. 91.3%. For graduate-level science questions, Opus still pulls ahead.

Root cause analysis (OpenRCA): 27.9% vs. 34.9%. Opus is significantly better at diagnosing complex software failures across enterprise systems.

ARC-AGI-2 fluid intelligence: 58.3% vs. 68.8%. For novel pattern reasoning, Opus keeps a healthy lead.

Codebase refactoring and multi-agent coordination: Anthropic specifically notes Opus 4.6 remains the stronger choice for tasks demanding “the deepest reasoning.”

For tasks that look like work (spreadsheets, presentations, data analysis, browser automation, tool use, financial research), Sonnet 4.6 is functionally interchangeable with Opus. For tasks that look like hard computer science (complex debugging, novel reasoning, large-scale code refactoring), Opus still has an edge.

As Alex Finn put it in his breakdown video: “Sonnet is not better than Opus at any specific thing, but it is just as good as Opus 4.6 when it comes to agentic tasks specifically. This is massive because it means it’s just as good as a brain for tools like OpenClaw and Claude Code… at a fifth of the price.”

What developers are actually doing with it

The response from the developer community was immediate and telling.

OpenClaw released a same-day update to support Sonnet 4.6, and users are reporting it as the new default model for their AI agent workflows. The logic is simple: if computer use and tool use performance is essentially the same as Opus, but the cost is one-fifth, you run Sonnet for everything except the hardest coding tasks.

Alex Finn’s breakdown laid out the practical decision framework: use Sonnet 4.6 as your main agent model, use Opus only for planning or one-shot implementations of complex components, and use Codex for pure coding tasks inside agent frameworks.

Meta Alchemist captured the consensus view: “Sonnet 4.6 feels like it was made for OpenClaw… with how much emphasis they put on running the apps on your computer, and tool usage. Almost the same levels there as Opus 4.6. If you are using Claude with OpenClaw, using Sonnet 4.6 will be faster and cheaper compared to Opus.”

Letta, the agent framework company, integrated Sonnet 4.6 and reported near-Opus-level performance on context engineering tasks with 70% better token efficiency. They did note one behavioral difference: Sonnet 4.6 is less likely to delegate work to sub-agents or explicitly trigger plan modes, so prompt tuning may be needed for complex multi-agent setups.

Cline 3.64.0 launched with Sonnet 4.6 support, highlighting clearer communication with the coding assistant, better framework integration, and improved codebase search.

The Firecrawl team identified what they called “the perfect web automation stack”: Sonnet 4.6 plus Agent Browser plus Firecrawl’s browser sandbox.

And Wes Winder offered the obligatory reality check in meme form: “Sonnet 4.6 just refactored my entire codebase in one call. 64 tool invocations. 1M+ new lines. 17 brand new files. It modularized everything. Broke up monoliths. Cleaned up spaghetti. None of it worked. But boy was it beautiful.”

The competitive landscape: How it stacks up against GPT-5.2 and Gemini 3 Pro

It’s worth putting Sonnet 4.6 in the broader competitive context, because this isn’t just an Anthropic-vs-Anthropic story.

On GDPval-AA (real-world knowledge work), Sonnet 4.6’s ELO of 1633 puts it ahead of GPT-5.2 (1462) and Gemini 3 Pro (1201) by meaningful margins. On the Finance Agent benchmark, it beats GPT-5.2 by nearly 5 percentage points. On DeepSearchQA (multi-step research tasks), it’s state-of-the-art across all models tested.

On traditional reasoning benchmarks, the picture is more competitive. GPT-5.2 leads on GPQA Diamond (93.2% vs. 89.9%) and MMMU-Pro with tools (80.4% vs. 75.6%). Gemini 3 Pro leads on MMMLU multilingual understanding (91.8% vs. 89.3%). But these are the kinds of academic benchmarks that, increasingly, don’t predict which model will be most useful in practice.

Where Sonnet 4.6 has a more unique advantage is in the infrastructure surrounding it. Programmatic tool calling, context compaction, adaptive thinking, and the computer use API are all capabilities that GPT-5.2 and Gemini 3 Pro either don’t offer or implement differently. For developers building agentic systems, these features often matter more than a few percentage points on a multiple-choice test.

One interesting data point from the system card: Anthropic measured how much “thinking” each model does on multilingual questions. Gemini 3 Pro used 1,078 tokens per question. Sonnet 4.5 used 437. Sonnet 4.6 used 246. Opus 4.6 used 191. GPT-5.2 Pro used 127. The models achieve comparable accuracy at wildly different levels of computational effort, which means efficiency (and therefore cost and speed) varies enormously even when benchmark scores look similar.

On the Petri open-source safety audit, which enables apples-to-apples comparison across different model providers, Sonnet 4.6 showed stronger safety properties than every API model from another provider that was tested, including GPT-5.2, Gemini 3 Pro, Grok 4.1 Fast, and Kimi K2.5.

How to think about this if you actually use AI for work

Here’s the practical takeaway, stripped of benchmark jargon.

• If you use Claude through the website or app: Sonnet 4.6 is now your default model. You don’t need to do anything. It’s faster and more capable than what you had yesterday, especially for file creation, data analysis, and any task that involves working with spreadsheets, documents, or web research.
• If you use Claude Code or agentic coding tools: Sonnet 4.6 should be your default for most tasks. Save Opus for complex architecture decisions, large refactors, or situations where you need the absolute best code quality on the first try. The 1M token context window means it can hold your entire codebase in memory.
• If you build applications on the Claude API: The combination of Sonnet 4.6 performance and programmatic tool calling is a meaningful cost reduction. Tasks that required Opus-class models (and Opus-class pricing) can now run on Sonnet. The 1M context window plus context compaction means you can build much longer-running agents without hitting limits.
• If you work in finance: This is genuinely the strongest AI model available for financial analysis tasks, including SEC filing research, financial modeling, and structured document generation. The benchmarks support this, and it’s rare for a Sonnet-class model to beat every Opus and GPT variant on a finance-specific evaluation.
• If you’re evaluating AI models for your organization: The GDPval-AA results are probably the most relevant benchmark to look at. It tests real professional tasks across real occupations, and Sonnet 4.6 is currently #1, slightly ahead of Opus 4.6. For most “knowledge work” use cases, this is the best value in AI right now.

Sonnet 4.6 is what happens when Opus-level intelligence meets Sonnet-level pricing, and it’s built from the ground up for the thing that will define the next year of AI: agents that actually do work on your behalf.

The age of AI as a “chatbot you type questions into” is rapidly giving way to the age of AI as “a coworker that uses your computer, reads your documents, and gets things done while you sleep.” Sonnet 4.6 is the model that makes that transition economically viable for everyone, not just the companies willing to burn thousands per day on API costs.

For most people, this should just be the model you use. No asterisks, no caveats, no “but wait for Opus.” Just use it.

Editor’s note: This content originally ran in the newsletter of our sister publication, The Neuron. To read more from The Neuron, sign up for its newsletter here.

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When the platform under a huge slice of the web changes its workflow, everyone feels the tremor.

WordPress is used by 42.6% of all websites, according to W3Techs. So even if only a fraction of those site owners adopt prompt-based editing, it’s still a meaningful shift in how everyday web publishing gets done.

According to The Verge, the new assistant is integrated into WordPress’ editor experience, including the site editor and media library. Instead of digging through menus to find the right setting, users can describe what they want and iterate from there, whether that’s rewriting page copy, translating text, generating or editing images, or making visual adjustments like changing fonts.

What prompt-based editing changes for teams

For small businesses, nonprofits, and anyone operating without a dedicated web team, the immediate value is momentum. Routine updates can become a tighter loop: request a change, review it, refine the prompt, publish.

That’s different from the traditional stop-and-start workflow, where a simple tweak turns into fifteen minutes of “where did that option go,” or a ticket that sits in someone else’s queue. And because many “quick edits” are actually growth edits, this also fits naturally into workflows tied to search visibility, including how teams already use AI SEO tools.

It also changes the skill that matters most. The advantage shifts toward people who can express intent clearly: the audience you’re speaking to, the tone you need, the elements that must not move, and the outcome you’re aiming for. In practice, that makes prompt-writing feel less like a trick and more like a new kind of web literacy.

For teams that want to get more consistent results, it helps to treat prompting like a process, not a one-off, which is why some are turning to prompt engineering tools to standardize and test what works.

The fine print

Prompt editing doesn’t remove responsibility; it rearranges it. When making changes is easy, reviewing changes becomes the job: catching tone drift, preserving accessibility, and confirming a “small update” didn’t quietly disrupt spacing, hierarchy, or mobile layouts.

That’s also where the productivity promise can wobble: when AI-generated output needs cleanup, teams can end up with more review labor, a pattern eWeek has described as workslop.

There’s also a sameness risk. If a lot of site owners ask for the same kinds of improvements using similar language, the web can start to converge on a familiar set of AI-friendly defaults. Teams that care about differentiation will want a lightweight playbook: brand notes, do-not-change rules, and a consistent review pass before anything goes live.

Also read: When AI-assisted updates scale faster than review, the web can wind up with more noise than signal, a problem that shows up at internet scale in AI slop.

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SpaceX is competing in a $100 million Pentagon prize challenge to develop software that allows battlefield commanders to control large fleets of autonomous drones using plain-language commands, according to Bloomberg.

The initiative, led by the Defense Innovation Unit, is designed as a fast-moving competition to build a voice-command layer capable of coordinating large groups of autonomous systems. Bloomberg reported that selected companies will move through phased testing, with the potential for follow-on defense contracts.

Beyond launches and satellites

SpaceX is expanding its defense footprint beyond rockets and satellite networks, entering the autonomous systems arena through the Pentagon-backed competition. The company is participating alongside its artificial intelligence (AI) affiliate, xAI, which recently merged with SpaceX to consolidate engineering and AI development under a single structure.

Following the merger, xAI has been building out teams capable of translating spoken battlefield instructions into machine-readable commands that autonomous platforms can execute in real time. The effort establishes SpaceX as a contender in the software layer that connects human command to coordinated system response.

When past statements meet present contracts

Musk’s deeper push into military AI marks a departure from his earlier public stance. In 2015, he signed an open letter calling for a ban on autonomous weapons and has repeatedly warned about the dangers of advanced AI.

The recent consolidation of xAI into SpaceX and the buildup of security-cleared engineering talent tied to government projects place those earlier warnings in contrast with the company’s expanding role in defense-related AI work.

Inside the Pentagon’s autonomous orchestration push

The program revolves around what officials call an Autonomous Vehicle Orchestrator, a control layer meant to convert a commander’s intent into coordinated action across distributed systems. Operators would no longer manually code each platform; the approach centers on tasking air and sea assets through natural language inputs, while keeping human decision-makers firmly in control.

The Defense Innovation Unit is leading the challenge with the Defense Autonomous Warfare Group and the US Navy, treating it as a rapid pathway to operational capability.

Companies advance through iterative sprints that test increasingly complex scenarios, and only those that complete each phase move forward. Firms that demonstrate performance can secure follow-on contracts, shifting effort from prototype testing to field deployment.

Other AI firms are also involved in the broader effort. Bloomberg reported that OpenAI is supporting selected defense technology partners with voice-to-digital translation tools, though it is not responsible for weapons integration or targeting.

Related reading: The Pentagon is reportedly reconsidering a $200 million deal with Anthropic over AI use limits.

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Axios reported that Defense Secretary Pete Hegseth is “close” to cutting ties with Anthropic and designating it a “supply chain risk,” a label normally reserved for foreign adversaries like Chinese tech firms. The reason? Anthropic won’t give the Pentagon blanket permission to use Claude for “all lawful purposes.”

Anthropic’s two red lines: no mass surveillance of Americans and no weapons that fire without a human involved.

The Pentagon’s response, per a senior official: “We are going to make sure they pay a price for forcing our hand like this.”

Here’s what makes this wild

• Claude is the only AI model currently running on the military’s classified systems.
• It was used during the Maduro raid in January.
• Pentagon officials openly praise its capabilities.
• The contract in question is worth up to $200 million, a fraction of Anthropic’s $14 billion annual revenue.
• But the real threat is the “supply chain risk” label, which would force every company doing business with the Pentagon to certify that it doesn’t use Claude (potentially knocking Claude out of Microsoft Copilot, for example).
• That’s a big deal, given that 8 of the 10 largest US companies already use it.

Meanwhile, OpenAI, Google, and xAI have all agreed to remove their safeguards for military use on unclassified systems. Anthropic is the only one holding out.

The Reddit reaction was immediate and almost unanimously pro-Anthropic:

1. Top comment on r/ClaudeAI: “This is a selling point. Make it an ad.”
2. Multiple users said they were upgrading their subscriptions specifically to support the company.
3. One defense contractor pointed out that many companies would rather drop their government contracts than rip Claude out of their workflows. The compliance costs alone aren’t worth it.

The irony is hard to miss

If any other model were as good, the Pentagon would just switch. The fact that they’re threatening punishment instead of walking away is maybe the strongest endorsement Claude has ever received.

As one Redditor put it: “This article is basically a billboard for Anthropic. ‘We’re so good the Pentagon can’t replace us even when they want to.'”

Elsewhere, the timeline is turning against Anthropic: developers have been prioritizing Codex over Opus after Anthropic made a series of unforced errors:

1. First suing instead of wooing (well, threatening to sue) the creator of OpenClaw over its previous name, Clawdbot, then losing out on hiring him.
2. The Super Bowl ads that general audiences ranked in the bottom 3% for likeability, but that drove an 11% user bump among those who got the joke.
3. Losing developer trust over a series of non-dev friendly moves re: using Claude Code in other tools (and just generally being the “premium priced” AI model).

What can we say? I guess Anthropic is the we-don’t-need-you-after-all AI company. We’ll see if that strategy works out for them…

Editor’s note: This content originally ran in the newsletter of our sister publication, The Neuron. To read more from The Neuron, sign up for its newsletter here.

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