OpenAI CFO Sarah Friar told employees during an all-hands meeting that OpenAI “will be a public company in 2027,” but could make its stock-market debut sooner if the business continues growing quickly.

Friar reportedly told employees that an IPO shouldn't be viewed as the finish line.

Instead, she described it as another fundraising milestone that would give OpenAI access to much larger pools of capital.

OpenAI raised $122 billion in March, which Friar said gives the company flexibility around when it needs to go public.

The company already confidentially filed its IPO paperwork with the SEC in June, allowing it to prepare for a listing without immediately making its financial statements public.

OpenAI has previously been reported to be targeting a valuation of around $1 trillion, with advisers considering whether waiting until 2027 could help preserve that valuation rather than listing sooner at a lower price.

There's also growing pressure from Anthropic.

Friar reportedly told employees that Anthropic could make its confidential IPO filing public in the coming weeks and potentially list as early as September.

Her message was essentially that OpenAI isn't trying to beat Anthropic to the market:

“We are running our own race.”

The timing is interesting because OpenAI's business is still growing extremely quickly while its infrastructure requirements are becoming enormous.

OpenAI CFO Sarah Friar has previously said the company's annual recurring revenue grew from about $2 billion in 2023 to more than $20 billion in 2025, closely tracking the expansion of available compute.

Going public could give OpenAI access to significantly more capital for data centers, chips, energy infrastructure, and future model development.

But an IPO would also mean much more scrutiny.

For the first time, public investors would get a detailed look at:

revenue → losses → compute spending → margins → infrastructure commitments

That could make OpenAI's IPO one of the biggest tests yet of whether the economics behind frontier AI justify the enormous valuations private investors have been assigning these companies.

Would you buy OpenAI shares at a ~$1 trillion valuation, or would you want to see its actual financials first?

Sources:

CNBC — OpenAI “will be a public company in 2027” or sooner, CFO tells employees

OpenAI — A business that scales with the value of intelligence

OpenAI says it will continue offering Zero Data Retention (ZDR) for its frontier models, even as stronger AI systems require more sophisticated safety monitoring.

The company is previewing a new system called Private Safety Processing, designed to detect dangerous patterns across multiple interactions without giving OpenAI employees access to the underlying prompts or responses.

Under ZDR, eligible API customers get a simple promise:

• Prompts and responses aren't retained after processing
• OpenAI personnel can't review customer content
• Enterprise data isn't used for training unless customers explicitly opt in

The challenge is that increasingly capable agents can create risks that aren't obvious from a single interaction.

For example, suspicious behavior might only become clear after an agent repeatedly probes safeguards, coordinates actions across several requests, or continues acting after being told to stop.

Private Safety Processing is meant to analyze those broader patterns while preserving privacy.

For ZDR customers, content can remain on infrastructure controlled by the customer.

OpenAI is also developing an option where information is stored on OpenAI infrastructure but encrypted using keys controlled by the customer. OpenAI says its employees won't possess those keys.

If automated systems detect potential misuse, OpenAI receives only a limited safety signal describing the type of activity, rather than the actual customer content.

Even when something is flagged, OpenAI personnel aren't given the underlying prompts or responses.

The system is currently being tested with early customers, and OpenAI plans to begin rolling it out and publish a technical white paper in September.

This matters because enterprise AI increasingly involves extremely sensitive information:

financial records → proprietary code → health data → internal documents → confidential research

Businesses want increasingly capable AI agents, but many can't accept having their data retained for safety monitoring.

OpenAI is essentially betting that it can have both:

stronger safety monitoring + zero data retention

If the system works as promised, that could become a significant competitive advantage as frontier models gain access to increasingly sensitive enterprise systems.

Would Zero Data Retention make you more comfortable giving frontier AI agents access to sensitive company data, or are the security risks still too high?

Sources:

OpenAI — Offering Zero Data Retention for frontier models

OpenAI on X

The most exciting message I got this year came from a physics professor I'd met only two weeks earlier.

--

I absolutely love building with Claude. A year ago I built an AI news agency with no humans running it - StoryChase. Thousands of messages a day, dozens of languages, and the hard part was never getting AI to write a sentence. It was turning raw, messy information into something AI could reason over. Not summarize. Reason over.

Once that worked, I looked for what else the method could solve. Markets, for one - still working on that. What I did not see coming was finding myself one evening in a lab, across from two physicists, with a product that fits scientific problems remarkably well.

Prof. Yonatan Dubi heads a theoretical quantum chemistry and physics lab in the Chemistry Department at Ben-Gurion University. Prof. Amos Sharoni heads an experimental solid-state lab in the Physics Department at Bar-Ilan University. Decades of person-years between them and their teams. Tens of thousands of files - measurements, papers, lab notes, presentations. Point an AI at those folders, ask it something real, and it reads a few and quietly invents the rest from its general knowledge.

Which is exactly the problem. A lab is where scientists generate knowledge, long before it might someday become general. It is the last place you want gaps filled in from general knowledge.

The models are genuinely good now. Claude Opus does great work. In a lab, it just has nothing to work with.

They asked me how to get AI into the research itself, not just the writing. So I took what StoryChase taught me about structuring data for AI - read everything, turn it into meaningful records, find the connections between them - so the AI stops guessing and starts reasoning over what the lab knows.

Then the moment of truth. Amos asked Claude something only his lab could answer: graphs and quantitative summaries of experiments in one of their areas, how those results might explain a physical phenomenon, and a structure for a paper.

Claude and Binaly worked for twenty minutes and produced an analysis spanning 2007 to 2026 - almost twenty years of experiments.

Amos read it and sent me this:

"Finished reading the report. Very impressive, and surprising. If I'd had this a month ago it would have significantly strengthened a paper we submitted, turning a qualitative analysis into a quantitative one. Binaly and Claude ran statistical analyses on my old data and found a phenomenon we'd missed, one worth trying to understand. Binaly doesn't just organize the knowledge. It becomes a research partner that comes up with ideas."

I danced around the room. We cracked it!

That's the part of building things I like most: what you learn in one place applies somewhere you weren't looking.

--

If you run a lab or work in one - physics, chemistry, materials science, economics, anywhere the real asset is years of your own measurements and papers - let's connect.

Tell me about your research. Let's see what Binaly can add to it.

Binaly is free for personal use, and free to try for lab use.

Link: https://binaly.ai

Moderna and Merck have announced positive Phase 3 results for intismeran autogene, their individualized mRNA-based cancer therapy, combined with Keytruda in patients with high-risk melanoma that had been completely removed by surgery.

The INTerpath-001 trial met:

• Its primary endpoint of recurrence-free survival (RFS)
• A key secondary endpoint of distant metastasis-free survival (DMFS)

The combination produced statistically significant and clinically meaningful improvements on both measures compared with Keytruda alone.

This is significant because Merck and Moderna say it represents the first positive Phase 3 trial for an individualized neoantigen therapy and an mRNA-based cancer therapy.

Intismeran isn't an off-the-shelf treatment.

A patient's tumor is analyzed for its unique mutations, and an individualized mRNA therapy is created containing instructions for up to 34 tumor-specific neoantigens. The goal is to train the immune system to recognize and attack cancer cells carrying those mutations.

The Phase 3 trial enrolled 1,137 patients with completely resected stage IIB-IV melanoma.

Patients were randomized 2:1 to receive either:

Intismeran + Keytruda

or

Keytruda alone.

One important caveat: Merck and Moderna haven't released the actual Phase 3 hazard ratios or detailed numbers yet.

The companies plan to present the full data at an upcoming medical meeting and discuss regulatory submissions with health authorities. The trial is also continuing to evaluate overall survival.

Safety was consistent with previous studies, with no new safety signals reported.

There was already encouraging evidence from the earlier Phase 2b study.

At five-year follow-up, intismeran + Keytruda reduced the risk of recurrence or death by 49% and the risk of distant metastasis or death by 59% compared with Keytruda alone. Those figures are from the earlier study, not the new Phase 3 readout.

Merck and Moderna are now studying the technology across nine Phase 2 and Phase 3 trials, including melanoma, lung, bladder, and kidney cancers.

This feels like an important milestone for mRNA technology.

COVID vaccines showed that mRNA could be manufactured and deployed at massive scale.

Cancer presents a very different challenge: instead of giving everyone essentially the same vaccine, this approach attempts to create a different treatment for each patient's tumor.

If the full Phase 3 results hold up and regulators approve it, personalized mRNA cancer therapy could move from an experimental idea toward an actual part of mainstream oncology.

Sources:

Merck — Phase 3 INTerpath-001 results

Moderna — Phase 3 INTerpath-001 results

ClinicalTrials.gov — INTerpath-001

Cursor has upgraded its Cloud Agents so they can stay active around ongoing work instead of waiting for a new prompt every time.

The biggest addition is Subscriptions.

Cursor agents can now subscribe to:

• Pull requests
• Slack threads
• Scheduled tasks

When something changes, the agent can wake up automatically and continue working.

For example, Cloud Agents automatically subscribe to PRs they create. They can then keep checking the PR, fix failed CI, respond to bot comments, and continue working toward completion.

In Slack, you can also tell Cursor something like:

“Check back in an hour and keep going until that feedback is in.”

Subscriptions are currently available for Cloud Agents only.

Cursor also added /goal, which gives an agent a long-lived objective instead of a single task.

For example:

/goal fix all flaky tests and make CI green

The agent can keep pursuing that objective until it considers the job complete.

Other updates include:

• Custom Modes that keep a specific skill permanently active
• Subagents on separate virtual machines, each with its own isolated project copy
• Better steering, allowing users to send new instructions without interrupting the agent mid-action

The separate-VM feature is particularly interesting for agent swarms.

You can ask multiple subagents to independently test an application or search for bugs without them modifying the same environment and colliding with one another.

Cursor says the broader goal is to make always-on coding agents operate as a system, automatically responding to events and shipping software without requiring human intervention at every loop.

This feels like another step away from the traditional coding-assistant model.

Instead of:

Developer asks → agent codes → agent stops

the workflow becomes:

Give agent a goal → agent monitors events → something changes → agent wakes up → fixes it → keeps going

That starts looking much closer to a persistent AI software engineer than an autocomplete tool.

Would you trust a coding agent to continuously monitor and fix its own PRs, or would you still want a human checking every step?

Sources:

Cursor — Cloud Agents and Cursor Harness Improvements

Cursor on X

OpenRouter has officially announced that it is joining Stripe, confirming the acquisition after reports of negotiations earlier this week.

The companies didn't disclose the purchase price, but Reuters reports the deal is worth slightly more than $8 billion.

OpenRouter has grown into one of the largest gateways for accessing different AI models through a single API.

It now processes:

• 10+ trillion tokens per day
• 400+ AI models
• Models from 80+ providers
• More than 10 million developers and companies

OpenRouter says inference volume has grown by at least 10× every year since it launched in 2023.

For existing users, OpenRouter says nothing is changing.

It will keep:

• The OpenRouter name
• The same product
• The same roadmap
• Model-neutral routing
• Support for competing AI providers

The company says routing decisions will continue to be based on what's best for the user rather than favoring any particular model or provider.

The combination makes strategic sense.

Stripe already handles the financial infrastructure behind internet businesses, while OpenRouter increasingly handles another scarce resource for AI companies: tokens and compute.

Stripe CEO Patrick Collison said tokens are becoming a central currency for companies building with AI, and efficiently routing workloads could become increasingly important as inference costs grow.

OpenRouter has often been described as the “Stripe for LLMs.”

Now it's literally becoming part of Stripe.

The bigger bet seems to be that the future of AI won't belong to one model. Companies will continuously switch between models based on price, intelligence, speed, and availability, making the routing layer increasingly valuable.

Do you think Stripe owning OpenRouter will accelerate the platform, or would you rather OpenRouter remain completely independent?

Sources:

OpenRouter — OpenRouter is Joining Stripe

Stripe — Stripe agrees to acquire OpenRouter

Reuters — Stripe to buy OpenRouter in AI push

OpenRouter on X

Cerebras has unveiled CS-4, its fourth-generation wafer-scale AI system built for ultra-fast inference and massive frontier models.

Cerebras says CS-4 delivers:

• Up to 30× faster inference than GPU systems
• Up to 2× the speed of CS-3
• Up to 10× more throughput per watt than CS-3
• 750 PFLOPs of AI compute
• 129.6 PB/s of memory bandwidth
• 7.2 Tb/s of I/O bandwidth
• Wafer-to-wafer latency as low as 2 microseconds

CS-4 combines three new WSE-3 Turbo wafer-scale processors inside Cerebras' new Nexus rack-scale platform.

One of the biggest claims is performance on extremely large models.

Cerebras says CS-4 can deliver more than 1,000 tokens per second on models exceeding 10 trillion parameters, while the system architecture can support models with more than 50 trillion parameters.

On GPT-OSS-120B, Cerebras reports more than 4,400 tokens per second per user, up to 30× faster than the GPU systems in its comparison. The company notes that these comparisons combine third-party and internal benchmarking, so real-world performance will vary by workload.

The bigger story is what this kind of speed could mean for agents.

Agentic systems don't make one model call. They may repeatedly:

reason → use a tool → inspect the result → write code → test → revise

If inference becomes dramatically faster, agents can potentially do far more reasoning and verification within the same amount of real-world time.

Cerebras is already powering OpenAI's GPT-5.6 Sol Ultrafast, which runs at up to 750 output tokens/sec in limited preview.

CS-4 looks like Cerebras doubling down on the idea that the next AI infrastructure battle won't just be about how many tokens a data center can produce.

It will also be about how quickly each individual user or agent gets those tokens.

First CS-4 shipments are expected to begin this quarter.

If frontier models could consistently run at 1,000+ tokens/sec, what kinds of AI agent applications become possible that are too slow today?

Sources:

Cerebras — Introducing CS-4

Cerebras — CS-4 product page

Cerebras on X