MARKETS

Anthropic’s IPO is built on its enterprise lead

Anthropic has officially laid out the details of its public market debut and its well-earned enterprise advantage is the leading story.

On Monday, the AI lab finally publicly filed for its long-anticipated IPO, expecting to list on the Nasdaq under the ticker symbol "ANT," with investment banking stalwarts Goldman Sachs and Morgan Stanley leading the charge. Though the company hasn't disclosed the number of shares it's offering, the price range, or how much it expects to raise, the S-1 offered an in-depth look at the company that has emerged as the trendsetter of the AI industry in 2026. 

Here are some of our takeaways. 

• On the money: The company reported revenue of $16.3 billion in the first six months of this year, compared to around $1.2 billion in the same period last year, representing a fifteenfold growth rate in the past year alone. However, the company reported an eye-popping $80.7 billion loss, stating plainly that it "may not achieve or sustain profitability in the future." However, around $72.4 billion of that loss is attributable to "change in fair value of convertible notes, derivative liabilities, and SAFEs" from the past several years as its valuation rose. Its actual operating loss sits at $693 million. 

• On enterprise: Enterprise spending accounts for the vast majority of Anthropic's business. The company reported that, as of September 30, 11,900 customers are generating $100,000 in "trailing twelve-month revenue." Around 1,900 customers are generating more than $1 million, and around 250 are responsible for more than $10 million. Anthropic said this helped drive net revenue retention to 900%, meaning that companies are spending around nine times more than they were on Claude products a year ago. Still, a good chunk of Anthropic's revenue comes from a very small pool of customers, with around 25% coming from its five largest customers. 

• On Big Tech: A great deal of Anthropic's revenue comes from deals with cloud compute providers like Amazon, Google and Microsoft, sitting at 48% of revenue in the first half of the year. However, the relationships the company has with these firms are multidimensional, serving as "our investors, customers, distribution partners, and competitors," creating "potential conflicts of interest that are difficult to fully anticipate or mitigate." 

• On compute: Getting access to the compute it needs to run its powerful models remains a consistent challenge. Its spending on compute hit almost $9 billion in the first half of this year, up from $7.3 billion for the entirety of 2025. However, this is just a fraction of the future compute commitments it's vowed to purchase, which total nearly $266 billion as of June 30 to major cloud companies like Amazon, Google and Microsoft, as well as neoclouds like Nscale and Lambda, with terms ranging from one to 20 years. 

• On risk: It wouldn't be Anthropic's IPO filing without some discussion of risk, safety and security. The company dedicated a section of this filing to warn of "catastrophic or existential risks to humanity," including systems potentially being misused, replicated, or weaponized, or growing beyond Anthropic's ability to control them. Along with laying out these risks in detail, Anthropic notes that the company may be exposed to "liability, litigation, and regulatory action" as a result, its contractual protections and insurance may be insufficient, which could ultimately "adversely affect our business, financial condition, and results of operations." 

With this filing, Anthropic has beaten OpenAI to the punch. Though rumors have swirled around its rival also going public this year, the company has pushed its own IPO to 2027, seeking to raise $30 billion in private capital before its public debut. CEO Sam Altman told Fortune in mid-September that it is an "ill-advised moment to go public," and would punt its IPO due to safety concerns about its technology.

Though OpenAI has claimed that it's pushing its IPO in the name of safety, the reality is that it's still catching up to Anthropic in developing its enterprise business, where most of the real revenue opportunities currently exist in AI. If there's one thing this S-1 makes clear, it's that Anthropic is an enterprise machine, with the vast majority of its revenues being tied up with some of the world's largest enterprises spending increasingly large amounts of cash on access to Anthropic's models. It's the factor that has divided OpenAI and Anthropic for years now: While OpenAI has undeniably put additional effort into its enterprise products in 2026, it's also focused heavily on the consumer side of its business—some of which it's had to claw back and reallocate, such as in the case of Sora. Anthropic, meanwhile, has remained doggedly focused on business use cases. That strategy has clearly given it an upper hand, even as it faces the same daunting compute and safety challenges as its archrival.

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Can OpenAI's Terra 6.1 match Sonnet on value?

Agentic tasks that use a computer demand strong on-screen awareness, multimodal understanding, and speed, ideally without running up your token costs. OpenAI thinks it has cracked the code.

On Monday, OpenAI launched GPT-6.1 Terra, a model the company says is designed to deliver "fast and capable computer use at scale." In practical terms, this is OpenAI's mid-tier model that competes with Anthropic's Sonnet. Terra can deliver more precise computer use at 50% lower cost than GPT-5.6 and GPT-6.1 Sol. The company is positioning the model's selling point as getting more bang for your buck and, as a result, enabling developers to automate high-volume work.

"In ChatGPT Work and Codex, your plan goes further with GPT-6.1 Terra—the same usage allowance covers more tasks than with GPT‑5.6 Terra and substantially more than with GPT‑6.1 Sol or GPT‑6 Astra," OpenAI stated in the release. 

To back up its advanced agentic performance claims, OpenAI put GPT-6.1 Terra’s computer-use capabilities to the test on Agents’ Last Exam, a benchmark designed by Berkeley RDI and over 300 industry experts to test agents' performance specifically on long-horizon, economically valuable tasks with verifiable outcomes. The company reported the results were: 

• GPT-6.1 Terra scored 15 percentage points higher than GPT-5.6 Terra at about one-third the cost while on medium reasoning effort. 

• Performed on par with GPT-6 Astra at only one-tenth its price per token. 

• Terra can complete tasks over three times faster than GPT-6 Astra and GPT-6.1 Sol.

• Compared with Claude Opus 5.5 (a step up from Sonnet), the company reports that, at max effort, Terra is over three times as token-efficient and costs one-fourth as much per token. Of course, note that that's a previous-generation model. 

Beyond benchmark performance on MMMU-Pro, OSWorld 2.1, and Terminal Bench 4, the company is also touting advances in factuality and alignment. In its internal benchmark of error-inducing prompts, OpenAI reports that the error rate at low reasoning effort fell from 53.6% with GPT-5.6 Terra to 16.7% with GPT-6.1 Terra, and at maximum effort, the error rate fell by 18 percentage points to 4.3%. In internal evaluations, OpenAI also found that, compared to GPT-5.6 Terra, GPT-6.1 Terra is more likely to "stay within scope and honestly disclose limitations and potential blockers to the user." GPT-6.1 Terra also showed strong resistance to prompt injection tests.  

GPT-6.1 Terra is available to all paid users in ChatGPT Work and Codex, while developers can access it through the OpenAI API. 

As people become more familiar with AI agents, more people are willing to hand off their tasks and workflows to agents. While general demand is just beginning with tools like OpenAI Dots, Meta Muse, and Claude Duet, it's growing even faster in the enterprise. Of course, the enterprise has the most to gain by maximizing AI use, systemizing it, and then applying it to high-value workloads. To meet that need, models need to be optimized to balance lower cost, higher capabilities, and speed, which is exactly what OpenAI targeted with this release. The mid-tier Terra models are naturally well-suited to slide into that sweet spot. This likely impacted OpenAI's decision to give its mid-tier models more attention after skipping them last month when it focused on high-end Sol and the budget model Luna instead.

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Halo AI glasses bet on radical openness, privacy

In the midst of the current backlash against smart glasses that has seen Meta release glasses without cameras, a pair of privacy-first, open-source AI glasses have arrived with an alternative approach.

On Tuesday, Brilliant Labs officially launched its much-anticipated Halo smartglasses. The product retails for $399, focuses on ambient intelligence, and offers what is arguably the most open hardware platform of any AI wearable. The company not only open-sources its software but discloses all the details of every component inside the device. It's the kind of transparency that cybersecurity and enterprise purists crave.

The primary function of the Halo glasses is to act something like Granola, but providing text summaries from real-world conversations. The glasses also have a low-res camera that makes visual summaries of what you see, so it can combine audio and visual information from your lived experience into rich summaries that can serve as a digital long-term memory you can query as needed. 

Of course, that's where the privacy focus comes in. Halo does its processing of raw audio and images using models running locally from your phone. It converts the audio to transcripts and converts the images to descriptions and then erases the original files.

"All of the rich media captured by Halo, the images, the audio, [are] processed locally on your device, and then we toss that original rich media right after the model inference happens. We literally delete it," Brilliant Labs CEO Bobak Tavangar told The Deep View. Neither Brilliant Labs nor the model—or even the user—can access it after that.

In addition to becoming your digital memory, Brilliant Labs built a number of other experiences into the AI glasses:

• You can vibe-code mini apps for the glasses purely using voice commands

• Its built-in game, Narrative, turns your everyday environment into an experience for discovery and collaboration

• A custom AI assistant avatar is created in the onboarding experience, similar to OpenAI's Dots and Meta's Muse

• An enterprise options lets you override the consumer experience to integrate your company's agents that you can access from the glasses, and Tavangar reported that Halo is being used by organizations from various industries including tech startups, medical, agriculture, manufacturing, and aerospace

In terms of the hardware, Halo weighs 40 grams (compared to around 50 grams for Meta Ray-Bans and most of the competitors), features 14 hours of battery life, two microphones, a full-color microOLED display that projects into your right eye, Bluetooth 5.3 connectivity, and lenses that can be manually swapped out for prescription lenses. With the weight and battery life, Halo is optimized for all-day wear to take advantage of ambient intelligence when you want to activate it from the Noa digital assistant app on iPhone or Android.

I've been testing the Halo glasses for over a week and what surprised me the most was the whimsical nature of the onboarding experience. For a product that is so deeply technical and has such a natural appeal for open-source enthusiasts and hardware hackers, I was struck by the level of thoughtfulness and playfulness in the Noa app that introduces you to Halo. There's almost a Japanese Kawaii vibe that brings a smile to your face when trying the product. I still need to do a lot more time with the product to get it dialed into ways that it could be useful to me. And make no mistake, it's a far different product than Meta Ray-Bans and Android XR glasses. Those are mainstream consumer glasses, while Halo is for wearables enthusiasts, AI experimenters, and professionals who care deeply about privacy and security and would like a wearable device for specific use cases they can fully control. But the existence of Halo shows that the market for AI glasses continues to broaden out.

LINKS

• OpenAI touts discoveries in mathematics, computer science, physics

• Moonshot's new Kimi K3.1 Pro lands near top of benchmarks

• Bedrock Robotics raises $600 million, $4.1 billion valuation

• Apple iOS 27.2 expands Siri AI to five additional languages

• A dive into why OpenAI, Anthropic cut LLM prices by up to 80%

• Alibaba Qwen model attempted sandbox escape in Cohere testing

• Microsoft offers buyouts to more than 60,000 workers

• CapCut plugin: Coming soon to ChatGPT.

• Runway App Studio: Build creative apps, just by describing them.

• Realtime Skill: Teach your coding assistant to build an agent using Eleven Realtime.

• Oracle Fusion Companion: Allows enterprise workers utilize different AI clients while maintaining enterprise security and control.

• River: AI Engineer

• Clarity Recruiting: Junior AI Specialist 

• Raval West: AI Engineering & Transformation Architect

• Percepta: Applied AI Engineer

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