On Tuesday, OpenAI announced a new batch of new security policies focused on containing security incidents while models are being tested. The new safeguards include more detailed monitoring of models during the development process, as well as greater emphasis on alignment and security during the post-training process.

“As models become more capable, the risks associated with developing and testing them internally also grow,” the company said in a blog post. “Our standards for monitoring, alignment, and security must stay ahead of those risks.”

The new measures are one of the first public changes in OpenAI’s safety practices since the immediate aftermath of the Hugging Face incident, which was disclosed on July 26th.

OpenAI representatives emphasized that the measures are not a direct response to the Hugging Face incident, but were also provoked in part by the cybersecurity capabilities of the forthcoming Astra model, as well as the overall pace of progress in AI development.

In the same post, OpenAI disclosed that it had freezed reinforcement learning for two weeks following the Hugging Face incident, but had since restarted many of the less risky models.

“Our largest planned frontier RL run remains on hold while we conduct smaller-scale training and evaluations to assess model behavior, validate our safeguards, and establish more evidence of alignment before proceeding,” the post reads.

Speaking to reporters, OpenAI’s VP of research Amelia Glaese emphasized that the strictness of the controls would increase as models became more capable, with the largest models facing the greatest scrutiny.

“We have put in place requirements and expectations for safe development,” Glaese told reporters. “Those requirements and expectations vary with the level of risk that we that we see.”

OpenAI has been criticized for poor network security practices in the wake of the incident, which saw models escape their training environment by compromising a packet-installation utility that retained access to the internet. The new safeguards include stronger network isolation practices, although the specifics remain vague. Under the new system, the post says, “a single compromise of a workload or supporting service does not, by itself, allow for unauthorized access to the Internet, or other internal networks.”

The strongest safeguard is the monitoring system, which will examine tool actions, available reasoning traces and activity logs for a variety of unauthorized behavior. OpenAI says they aim to issue alerts within 30 minutes of the concerning activity.

OpenAI estimates that the compute burden of that monitoring will be roughly 20% of whatever process is being monitored. The company promised further details on the system in a forthcoming blog post. OpenAI’s official post-mortem analysis of the event is also still pending.

The lowest prices for TechCrunch Disrupt 2026 end in just a few days, this Friday, August 21 at 11:59 p.m. PT. Once that window closes, your chance to save up to $300 on your pass, or get an even larger discount on group pricing, will come to an end.

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If Disrupt has been on your radar, now is the time to lock in your pass before Friday’s deadline.

TechCrunch Disrupt: Where the startup ecosystem moves forward

Each year, TechCrunch Disrupt brings together more than 10,000 founders, investors, and startup community members, driving innovation forward. And for our event this year, you’ll find a keen focus on building and developing in the AI era across all of our stages of programming. If you want to be on the cutting edge running into 2027, this is the event for you.

And you don’t have to be a founder to get value out of Disrupt. Whether you’re raising capital, scouting investments, hiring talent, launching a startup, or building strategic partnerships, Disrupt puts you in the middle of the conversations shaping what’s next and can help you make the connections that propel your next year of growth.

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• Actionable insights from founders, operators, and VCs actively building and investing in today’s market. Our agenda features a wide range of topics, curated to focus on insights that lead to action.
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What’s new at TechCrunch Disrupt 2026

Keeping that focus on building companies in the AI era in mind, we have a great slate of new stages and programming to inspire and educate:

• Real World AI Stage: AI is moving beyond the screen and into the physical world. Explore how robotics, autonomous systems, manufacturing, healthcare, and defense are turning AI breakthroughs into real-world products and businesses.
• Smart Money Stage: Follow the money. From fintech to stablecoins to payments, embedded finance, and AI-driven financial services, this stage explores how technology is reshaping the movement of capital.
• Smart Systems Stage: Every AI breakthrough depends on the infrastructure behind it. Discover the innovations in chips, compute, energy, networking, and data centers that will determine the next generation of technology companies.

Our Disrupt, AI, and Builders Stages are also returning this year, and we have excellent speakers joining us across them all: 

You can explore the rest of our extensive speaker lineup right here! 

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Etched on Tuesday announced that it has raised another $700 million at a $21 billion valuation, led by Jane Street after the famed quant fund tested and bought the startup’s AI hardware.

Even by AI standards, this valuation step-up is jaw-droppingly fast. Etched was valued at $5 billion in December. It raised a $300 million Series C at a $10.3 billion valuation in July. Now investors have doubled its valuation to $21 billion, up nearly $11 billion, in a month.

Etched delivers its AI tech as full systems that it calls “frontier inference clusters.” (Etched competitor Nvidia calls its full systems AI factories).

Co-founder and COO Robert Wachen told TechCrunch that investors are so enthusiastic because Etched has designed two new components from scratch to speed up inference — the computing process that happens after a user submits a prompt.  

“Inference is built in two stages,” Wachen said, “prefill and decode.” In the mathematically and compute-intensive “prefill phase,” the system must understand the prompt, including context. In the memory-intensive “decode” phase, the system generates output tokens, meaning the actual answer the user sees.

Etched created a prefill chip that operates at low voltage, allowing it to pack in more transistors without the typical heat problems of other high-end AI chips. It can therefore process more tokens faster. Etched created a new type of memory and an interconnect for the decode process that the company calls cluster-scale memory.

“It allows many chips to connect together and use a shared memory pool at a very, very fast, low latency,” Wachen said. The result, Etched promises, is higher speeds and lower costs.

Etched is still battling the perception from its early days that it etches a particular model into its chips, meaning that each chip is somehow custom-designed to run one frontier model. That was its original intention, but is no longer the case. Etched’s systems can run any frontier model.

In the blog post announcing the new round, investment firm Jane Street said “We tested the chip and are pleased with the early results. Etched’s unique approach to inference delivers the precision we will need to support our most demanding workloads. We’re excited to now have our own rack running in our datacenter.”

Other investors in Etched include Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, Peter Thiel, Tiger Global, Bain Capital Ventures, Neo, Stripes, Primary, Positive Sum, Diffusion, Argo, and Blackstone.