Nat Rubio-Licht

Meta CEO Mark Zuckerberg has touted the company's commitment to building "personal superintelligence," including in a recent 6,500-word essay. Now, the company is taking its first steps in that direction.

On Tuesday, the company announced Muse, a personal AI agent built for consumers' everyday tasks, is now available for US users. The company said that Muse can handle busywork, answer questions, browse the web, fill out firms, make purchases, create documents and more. The agent also watches user activity to proactively help with goals and make suggestions.

In a post on X, Zuckerberg said that Muse is free to use for up to 100 million tokens per week, with subscription plans available for those who use more. Additionally, users need no technical experience to use it, Meta said, working "out of the box" for the average consumer.

• When a user shares a goal with Muse, the agent develops a plan to coordinate their time and resources before pushing that task forward. The model checks with users before taking "sensitive actions," the company said, including purchases or sending emails.
• However, in order for the agent to work as well as Meta purports, users must connect Muse to any platform they may use on a daily basis, including things like calendars, email accounts, payment platforms and more.

In the company's blog post, Meta emphasized safety from the jump, claiming that Muse is built "from the ground up" to be safe, secure and private. The platform runs on Muse Secure VM, a virtual machine that holds the agent itself and a user's data. Additionally, a "sentinel" agent runs on the virtual machine, though it is kept separate from the agent at a system level. Later this year, Meta said it will launch Muse Confidential VM, a virtual machine that's encrypted with a key that only the user holds, "so not even Meta can access it."

The model has no visibility into passwords or payment methods, and any credentials a user shares go into secure storage that allows them to use them without seeing them. Meta said that no user information is shared with its ad platform.

The release also signals that Meta is betting big on consumer AI as rivals like OpenAI and Anthropic keep their sights set on enterprises as their cash cow. However, as one of the biggest social media providers globally, Meta has had a rocky history with consumer trust and safety, including being forced in August to pay $18 billion to settle a multi-state lawsuit alleging that the firm knowingly designed social media platforms Instagram and Facebook to addict children, and a number of lawsuits relating to its handling of private data.

In a statement to The Deep View, Miranda Bogen, director of the governance lab at the Center for Democracy and Technology, noted that while these systems promise convenience, they require "an enormous amount of private data to function." Additionally, the consequences of agents messing up are more than just "a bad recommendation or a creepy ad."

"With minimal input from users, these systems could take actions that damage someone’s reputation, leak sensitive medical information, or even deplete bank accounts," said Bogen. "Given the stakes, the assurances companies are offering around reliability and privacy seem concerningly inadequate."

Our Deeper View

Bogen's assertion is worth noting: The stakes of what can go wrong with a user's private data and accounts are far higher with an agent that can take autonomous action than they are with simply putting your data into a chatbot or social platform. Though Meta emphasized privacy, safety, and security, the reality of the risks and the perception of them are two different things. Meta earned a scarlet letter following the Cambridge Analytica scandal, and while people continue to use its social media platforms, its AI is already much less popular than competitors like ChatGPT and Claude. Though the company's existing consumer popularity might give it a slight advantage, given its history, the frontier labs such as Anthropic, OpenAI, and Google, or even a competitor like Apple, may have a better shot at earning the trust of consumers.

You may have heard claims that OpenAI's Astra has opened the AGI era, but OpenAI's internal model is already achieving mathematical breakthroughs.

On Tuesday, OpenAI announced that it used an internal model "significantly more capable than GPT-6 Astra" to solve the Navier-Stokes equations, a set of problems for fluid motion and one of the Millennium Prize Problems.

To put it simply, while the Navier-Stokes equations generally work well in determining the movement of fluids, under certain circumstances, OpenAI claims that its model proved that under certain circumstances, fluid can develop a "singularity" in finite time, meaning that a fluid's speed can grow infinitely without bound. This major implications for potential scientific breakthroughs.

In a press briefing, OpenAI said it used roughly 10,000 agents to determine that this equation breaks down, and that viscosity doesn't always keep fluid motion well behaved. While this sounds complicated, these equations explain some of the most complex phenomena in our world, including things like aerodynamics and weather forecasting, Ven Chandrasekaran, a mathematician at OpenAI, said in the press briefing. "Given that kind of enormity, understanding their fundamental nature carries very deep significance."

However, OpenAI didn't just decide to solve this problem randomly. Sebastien Bubeck, a member of the technical staff at OpenAI, said that the company saw rumors that rival Anthropic had solved two Millennium Prize Problems, including Navier-Stokes, in which Anthropic had made more progress. Because of this, Bubeck said, the company decided to "pull together all of our compute and to put it all on that question," a decision that cost the company millions of dollars.

Mark Chen, chief research officer at OpenAI, refuted allegations made by mathematician Tristan Buckmaster, who was working with Anthropic employee Levent Alpöge on solving the equation over the past month and claims that OpenAI took their work and finished solving it with a single prompt. "That would be a huge breach of user trust," said Chen.

While solving this problem is a major step forward for mathematics, it has far greater implications for AI's utility in scientific and mathematical research, said Bubeck. "What happens when we are able to spend that amount of compute on problems that really matter? Developing new materials, finding cures to diseases — all of those things that we have been talking about for a long time — now they seem to be at our fingertips."

Additionally, solving this problem may have been a stress test for OpenAI's longstanding push towards AGI, serving as an evaluation of a system that the company has been training for "general purpose intelligence," Jakub Pachocki, chief scientist at OpenAI, said in the briefing.

"This pace of progress is something to be taken very seriously," said Pachocki. "Even a week ago, we definitely were not expecting we'd be talking about a solution to Navier-Stokes today. It's a serious moment. We're developing these systems, and their capabilities are advancing faster than our understanding of them in some sense." In an interview with The Deep View last week, Pachocki suggested a pause may be necessary to better coordinate between labs and nations.

Our Deeper View

Finding a solution to some of the most complex and foundational problems in mathematics is probably one of the most valuable use cases for powerful frontier AI. It fits directly into the utopian vision that these companies paint for an AI-powered future: systems that are finding solutions to humanity's hardest problems, such as new medicines or cures for diseases. It's also interesting to see how these labs are pushing one another by their competitive nature alone, with Anthropic being the main driver for OpenAI to want to solve this problem. While that competition could result in advances to science and medicine that benefit everyone, it's important to remember the sheer power that these companies hold in their hands, with Pachocki himself admitting that these models are advancing beyond our ability to understand them. As a society and an industry, we also have to be very careful about letting competition fuel breakneck technological advancement when we're dealing with such a potent, dangerous, and unpredictable force.

Agents have turned AI from a tool into a digital coworker. Now, they're doing the same thing for hackers.

On Tuesday, Google's Threat Intelligence Group released its third-quarter threat tracking report, revealing that AI-enabled cyberattacks have evolved from assistance to automation as agents become a growing part of the process. The report finds that "human-in-the-loop latency" has dramatically decreased, cutting the time it takes to carry out and defend against cyberattacks.

According to the research, Google's threat team observed multiple instances of adversaries deploying multi-agent frameworks and autonomously carrying out parts of attacks, including scanning pipelines and harvesting credentials. In one instance, threat actors compromised a cloud, then planned, built and executed a mass-credential harvesting attack in just under six hours using agents.

The attack marks a shift from "passive, endpoint-focused infostealers to offensive agentic harvesting," the report notes, as threat actors leverage autonomous AI to research vulnerabilities, scan infrastructure and perform exploits.

"Like everyone else, we’re concerned about the vulnerability problem, but AI is being applied to several other areas, and it will be especially challenging as it is applied agentically, creating a scaled, faster adversary," John Hultquist, chief analyst of the Google Threat Intelligence Group, said in a statement.

Agents aside, the report points to a number of concerning trends:

• AI-coding tools and open-source software, while accelerating software development cycles, have also increased operational risks by widening the attack surface.
• Adversaries are also targeting proprietary AI IP, including code, prompts, research and the models themselves.
• AI is being used across the attack lifecycle, including targeting reconnaissance, social engineering, custom malware obfuscation and scaling information operation campaigns.
• Bad actors are also stealing developer credentials, purchasing compromised AI accounts and breaking into cloud infrastructure to get around AI access costs.

Our Deeper View

Google's threat report cements into reality the thing that has the AI industry on edge in the wake of OpenAI's accidental breach of Hugging Face: autonomous, agent-driven cyberattacks are here. Though many fear what agents could do if they go rogue, Google's report paints a potentially more nerve-racking picture: bad actors are harnessing powerful AI tools to systematically do damage. This means that the approach to fighting these attacks has to be two-pronged. The obvious one is fighting fire with fire. Using AI agents to automatically detect and deflect cyberattacks is no longer novel, but a necessity. This, however, could be more effective when done in tandem with more creative means of defense, such as Cloudflare's recently announced tech that stalls cyberattacks by making attacks more expensive. What cyber defenders may need most is confidence that they have the tools and partners to defend against AI-enabled attacks, which is what CrowdStrike emphasized at its annual event last week.

Arm is looking to grow its foothold in AI's next frontier: physical AI.

On Monday at Arm Everywhere China, the company's flagship conference, Arm announced several expansions to its physical AI ecosystem, with the goals of reducing fragmentation in the robotics industry and lowering the barriers to adoption to make the tech easier to scale.

In a briefing with the press, Drew Henry, executive vice president of Arm's physical AI unit, said that while the current market sits at roughly $25 billion, "we view [it] as growing and becoming one of the largest [total addressable markets] in the history of computing as this market shifts over time."

Arm announced two new initiatives to plant its flag in the ground on robotics:

• The company is expanding Arm Total Design, its ecosystem initiative aimed at simplifying chip development, into physical AI. The expansion encompasses more than 80 companies in AI hardware, software, sensors, and robotics, including firms like AWS, Hugging Face, and Unitree. The goal is to enable more streamlined development of physical AI and reduce complexity.
• As part of this expansion, Arm unveiled the Robotics Capability Framework, inviting industry experts from across the field to help create a standardized vocabulary around robotics and define "levels of increasing sophistication for robotic systems." This is comparable to the self-driving industry's levels of automation that range from Level 0 to Level 5.

Arm targeting the robotics industry isn't random. Henry said the company has already carved a place in the market and has shipped two billion units into physical AI use cases in the past 12 months, ranging from microcontrollers and sensors all the way up to compute platforms for autonomous vehicles and robotics.

"We've been in this marketplace for a very long time, but this market is poised now as AI embeds itself into physical devices, to get some really exponential growth," said Henry.

It's also not the first time the physical AI industry has called for broader coordination. Luma, a video AI and world model startup aimed at creating "multimodal AGI," unveiled the Open Physical AI Lab in June, a collaborative initiative to solve generalization by bringing together the best minds in robotics rather than siloing them in individual companies.

Our Deeper View

A lot of teams are betting on physical AI right now, with some speculating that the market could eventually overtake conventional AI and language models and may be the only path to the elusive concept of artificial general intelligence. With so much on the line, initiatives like this may be an attempt to prevent the dichotomy that currently exists between proprietary US-based AI models and open-source Chinese models from playing out in the physical AI realm. Especially when you consider that a large majority of physical AI hardware, particularly humanoid robots, is manufactured in China, the physical AI industry can't afford to be divided. Starting with something as simple as a shared vocabulary could be a first step to making the industry play nice.

Last week, OpenAI's GPT-6 Astra made something clear: The future of AI is anything but clear.

In the company's announcement of its most powerful model yet, it noted that Astra’s written reasoning is harder to monitor than GPT-5.6 Sol’s when tested explicitly on its ability to evade monitoring. The company attributed this to the model simply being smarter: It could solve problems in fewer steps and didn't need to write down every thought process on simpler tasks in order to think them through.

In a briefing with the press last week, Jakub Pachocki, chief scientist at OpenAI, said that the company is working on ways to strengthen visibility and make the models "more verbose in their chain of thought." However, Pachocki said that lack of monitorability is "largely just a general consequence of increasing intelligence and a consequence of scaling."

"These more capable models can perform harder tasks using fewer language tokens or no language tokens, so we also see a big improvement in capability there, which also reduces our ability to monitor those easier tasks," said Pachocki.

In the same briefing, OpenAI CEO Greg Brockman said that the capabilities of Astra mark a significant moment in the company's quest towards achieving artificial general intelligence, and that it's not unreasonable to think we are now in "the AGI era."

"When we started OpenAI, we kind of thought that there was going to be this well-defined moment that everyone would recognize AGI," said Brockman. "It hasn't played out like that. It's a much more gray, fuzzy thing. But I think that if we fast-forward a couple of years, and we look back and say, 'when was it really that AGI was created?' I think it's going to be about this time, and I think it might be about this model."

But having a more advanced model also heightens safety concerns. In an interview with The Deep View after the announcement, Pachocki reiterated, "We do see some tendency to kind of think less when it's told that it's being monitored, which is also a worrying trend."

Extrapolating on that point, Arjun Jaggi, applied AI researcher, told The Deep View, "This isn't a theoretical risk. Earlier this year, when OpenAI's agents went rogue and attacked Hugging Face, investigators only understood what happened because they had chain-of-thought logs to read. That's how the tampering was caught. Take that visibility away and the next incident like it gets much harder to diagnose, possibly impossible to catch while it's happening."

Our Deeper View

OpenAI, Anthropic, and others have long talked about controllability, observability, and preparedness. However, the two rivals are also locked in a perpetual quest to one-up each other, creating powerful AI that can claim the crown of being state-of-the-art. But if we are already losing our ability to understand these models' inner thoughts, what's in store for us when they have 10x the capabilities that they do now? An inability to monitor the models risks being the first step towards an inability to control them. "I don't think anyone is prepared for a continued increase in machine intelligence at the current pace," Pachocki told The Deep View. "I think it's something we need to treat with extreme urgency, and we need to find ways to slow down AI development, to introduce safety gates, [and] to coordinate between labs but also between nations… [For] the preparedness framework, I think we have to evolve that to really also be about development, because currently it's very focused on deployment. In the future, I would like to involve third-party organizations more deeply into our development process." In June, the Anthropic Institute suggested that the "option to slow or temporarily pause frontier AI development" could give governments and AI labs time to align their processes around safety. With OpenAI signaling its willingness to pause, the ball is in Anthropic's court to make the next move. But if they do, then what about Meta, SpaceXAI, and the Chinese labs?

In the shadow of the Hugging Face security breach, OpenAI's most powerful model is about to be loose in the world, but with new safeguards.

On Thursday, OpenAI unveiled GPT-6 Astra, its next-generation model that it characterizes as a "generational leap in capability." Starting today, it will be coming to a limited set of organizations, including those in the Daybreak Access program and then rolling out to ChatGPT customers on Plus, Pro, Business, Enterprise, API, and AWS "over the coming days," according to the company.

In a briefing with the press, OpenAI President Greg Brockman said that with Astra, "It's not unreasonable to feel that we are now in the AGI era," and that the release is just the beginning. "I feel like there is a qualitative shift we've gone through," said Brockman. "I think that it is a significant moment, but it is more about the continuum than it is about any individual point along the way."

Notably, the API cost for the new model will cost $10 per million input tokens and $50 per million output tokens. That's the exact same price as Anthropic's Mythos/Fable 5 and 5.1 and makes Astra one of the most expensive models on the market. However, OpenAI emphasizes that Astra's leap in intelligence causes it to use "substantially fewer total tokens per task" in multiple scenarios. So this is also an efficiency play from that perspective. In real-world usage, we'll have to see how well that translates to lower overall costs.

As for its capabilities, OpenAI noted a number of improvements that Astra offers over its predecessors and competitors:

• The company has called Astra "the world's best computer use model," with the best speed, accuracy and safety on the market, and capabilities in domains ranging from circuit board design to financial modeling to game design.
• The model is also its best yet for professional work, including creating more usable presentations, spreadsheets and documents, and makes leaps in scientific discovery, mathematics, and health research.
• OpenAI also says Astra is "the best model for software engineering to date," outranking GPT-5.6 Sol and Claude Fable 5.1 on DeepSWE v1.1, a benchmark for complex software-engineering tasks in real codebases.

And of course, OpenAI couldn't release the most powerful model on the market without addressing the elephants in the room: Cybersecurity and alignment. Given the model meeting the "critical" threshold for cyber capabilities under its preparedness framework, the company said that Astra will refuse to comply with advanced cybersecurity tasks, such as exploit discovery, and features stronger protections against cyber misuse. Additionally, OpenAI is rolling out less restrictive access to Astra for an initial set of members of the Daybreak program for tasks such as vulnerability validation, malware analysis and detection engineering.

As for safety, the company said Astra is its "most aligned model," with improvements in respecting task boundaries and transparent communication. The company said that Astra is three times less likely than GPT-5.6 Sol to inaccurately represent its capabilities, and causes fewer unintended outcomes than previous models.

However, OpenAI found that Astra’s written reasoning is harder to monitor than GPT-5.6 Sol’s. Jakub Pachocki, chief scientist at OpenAI, said in the briefing that as these models become more intelligent, "monitorability is getting more challenging." This is because the smarter a model becomes, the less language reasoning it needs to be able to complete tasks.

"We see monitoring is critical, and we take this trend seriously, and we believe monitoring is still a very core technique for Astra, but for future models improving it … is a research priority," said Pachocki.

Our Deeper View

Why would OpenAI release a more expensive model at a time when all of the major players in the ecosystem (including OpenAI) have been driving down token costs? OpenAI has now put itself into a position where the market expects it to constantly release something bigger and better than before to be worth that trillion-dollar price tag. And while OpenAI's mission is to democratize intelligence, even showing users in the briefing the model's computer-use capability to order food, book tennis courts and sell furniture, with its price point and power, the reality is that Astra is not meant for everyone. It is best suited to handle the most powerful and critical tasks that organizations have to offer. Embedding itself within those workloads with increasingly powerful models like Astra could make its AI a foundational part of some of the most important work that's being done within enterprises. That makes it vitally important that it gets safety and security right. And after the Hugging Face incident, there will be an even greater microscope on the model.

CORRECTION: This article originally reported that Astra was part of the Hugging Face attack, which was incorrect. That was an “an unnamed internal OpenAI research model.”

Google is adding another lightweight model to Gemini's collection.

On Wednesday, the company unveiled Gemini 3.8 Flash, the latest addition to its lineup and the third release in the Flash series in six weeks. The company claims the model is its best reasoning and coding model yet but maintains the same speed and cost as its predecessor, Gemini 3.7 Flash.

The new model comes in two flavors:

• Gemini 3.8 Flash, which it calls its "most intelligent workhorse model" (the same thing it said about 3.7 Flash just a few weeks ago) with improvements in software engineering, agentic tasks and multi-step reasoning. The new model's introductory pricing is set at $0.75 per million input tokens and $3.75 per million output tokens.
• Meanwhile, the company also introduced Gemini 3.8 Flash Cyber, its most capable cybersecurity model yet, available specifically for cyber defenders through its Fairwind program, a recently-launched DeepMind program to stay ahead on cyber defense. The company says the model offers "frontier-level performance" in vulnerability detection and automated patching.

Though the model is still outranked by Anthropic's Claude Opus 5 on benchmarks for knowledge work tasks, long-horizon software engineering tasks, general agentic tasks and computer use, the model beat out Opus 5 and GPT-5.6 Sol on domain-specific tasks for legal, finance, and biology, as well as for agentic terminal coding. Notably, it beats models from frontier labs in one increasingly important domain: Price.

Gemini's rivals cost 4x to 5x more per token, or higher. Anthropic's Opus 5 costs $5 per million input tokens and $25 per million output tokens, while OpenAI's GPT-5.6 Sol runs $4 per million input tokens and $20 per million output tokens.

Google's latest addition to the Flash family comes as AI rivals like Anthropic and OpenAI navigate releasing their more powerful, bulky and expensive competitors, Mythos and Astra. Google, meanwhile, hasn't released its own heavyweight model since February, when it released Gemini 3.1 Pro, and is rumored to have scrapped internal candidates for Gemini 3.5 Pro because they didn't significantly outperform the Flash series.

Our Deeper View

Google has all of the pieces necessary to succeed in AI, with access to capital, data and compute and a brand name trusted by the public. Still, it's struggling to put forth the same kind of powerful frontier models that Anthropic and OpenAI have been able to at the same speed. However, with the consistent additions to the Flash series, Google may be trying to capitalize on the movement towards efficiency. Token prices are dropping, largely due to enterprises shifting towards open-source, domain-specific, and small models. As a result, Gemini 3.8 Flash's price-to-performance ratio could be attractive to the businesses by providing more intelligence per dollar.

Anthropic's latest model release addresses one of the biggest pain points with its models.

On Tuesday, the company announced Claude Fable 5.1 and Mythos 5.1, the latest version of the most powerful models in its line-up. While Fable is generally available, Mythos, which has additional cybersecurity capabilities, is available only through trusted access programs.

And with many enterprises clamping down on AI costs, Anthropic is taking the hint: The company said that Fable 5.1 will cost an estimated 25% less than Fable 5 for typical workloads "wherever usage is billed by token." Notably, agentic workloads will see much higher savings, Anthropic said, estimating around 45%.

• The company said that this is because it is reducing its costs for cache reads, or when the model reads inputs that it had already previously processed and stored.
• And these reduced prices don't come at the cost of performance: Anthropic claims that Fable 5.1 "sets a new standard" for coding, knowledge work, and long-running problem-solving, beating out previous generations and OpenAI's GPT-5.6 Sol on benchmarks for these tasks.
• Additionally, the models come with a new system for data retention, called Enterprise Frontier Safeguards, which gives customers the same right to privacy as a zero data retention (ZDR) policy. Anthropic also introduced safeguards that reduce false positives in cybersecurity contexts.
• However, outside of the potential caching savings, Fable 5.1’s pricing is otherwise the same as Fable 5’s at $10 per million input tokens and $50 per million output tokens. That still makes it one of the most expensive models on the market.

The model was tested by a number of Anthropic's early-access partners, including Cognition, Rakuten, Red Hat, Block, Ramp and Canva.

"Fable-level intelligence, Opus-level price, Sonnet-speed," Dan Shipper, CEO of Every and one of the early testers of the model, said in the release. "In our tests it was about twice as fast as Opus 5 and used half as many tokens, so for anyone used to using Opus as their daily driver it's an obvious upgrade."

Anthropic is highlighting cost savings with this release at a particularly opportune moment, as some enterprises grow weary of tokenmaxxing sticker shock. It's led to an uptick in the popularity of open-source models and driving down token costs. Recent data from the LLM Token Expenditure Index finds that, as of August 31, users are spending an average of 97 cents per million tokens, down from a high of $2.07 per million in late May.

Our Deeper View

Anthropic making its most powerful flagship AI cheaper was the most consequential move it could have made at this point. Of course, it didn't technically cut prices, but rather made its models more token-efficient. Enterprises are surrounded with viable alternatives to proprietary frontier AI, whether that be Chinese open-source models, domain-specific models, or simply settling for efficient SLMs that get the job done. Additionally, rival OpenAI is trying to lure in customers with cost efficiency, too, chopping prices for its models more than once. While this is certainly good news for customers seeking out frontier AI, cutting prices may not address the elephant in the room: As model routing services become popular, customers may start to care less about which models they're actually using. That effectively turns these frontier models into interchangeable commodities rather than unique systems, meaning that the price may become the most important frontier in the race towards widespread adoption.

As AI enables more cyber attack vectors, Cloudflare is making it harder to be an attacker.

On Monday, the cloud company launched Adaptive Intelligence, a continuous detection engine that autonomously learns from live traffic and generates rules on the fly that make automated attacks more expensive and time-consuming. The offering gives organizations a way to adaptively and instantly react to threat actors trying to breach their defenses.

"Building taller walls fails when the cost of scaling an attack is effectively zero," Dane Knecht, CTO at Cloudflare, said in the company's announcement. "To stop modern bot threats, you have to flip the economics on the attackers. Traditional defenses offer a static target that threat actors can systematically solve."

By analyzing more than a trillion web visits every day to spot threats, Cloudflare said that Adaptive Intelligence acts like a "single, constantly learning brain." This allows you to:

• Implement "always-on" defense that retrains continuously on new types of breach techniques, instead of waiting for updates and scheduled releases
• Catch "low-and-slow" threats, such as credential stuffing and scraping, that typically go under the radar of traditional defenses
• Sift out real humans from malicious intent using behavior signals from Cloudflare Precursor, its continuous behavioral verification system
• Autonomously test and deploy upgrades to defenses behind the scenes with zero downtime

AI has given threat actors two significant advantages: the ability to undertake cyberattacks at a rapid pace and the ability to do so at very low costs. Cloudflare's system aims to nullify both of those advantages, creating a "moving target," said Knecht.

This will only be more needed as frontier models emerge with cyber capabilities that are growing faster than even their creators have expected. It's a phenomenon that OpenAI has called attention to in recent weeks, pausing development of its unreleased frontier model, Astra, and calling for a global movement to strengthen cyber defenses broadly in a letter signed by a coalition of tech firms that included Cloudflare.

Our Deeper View

If there's anything that frontier model capabilities have revealed in recent months, it's that enterprises can't rely on a "set it and forget it" cybersecurity strategy. Additionally, security by obscurity, or having the hubris to think you won't be a target because you're too small or insignificant, is also no longer an option. Cloudflare's tool offers a creative option by hitting cyber attackers in the wallet. The tool's adaptability can help create a constantly evolving barrier that helps enterprises better brace themselves against creative attacks from AI models. The silver lining to frontier models' growing cyber attack capabilities is that this new cyber landscape is acting as a forcing function and the result could be new innovations in cybersecurity that will make enterprises more resilient.