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Forget About Cloud Computing. On-Premises Is All the Rage Again

Ten years ago, everybody was fascinated by the cloud. It was the new thing, and companies that adopted it rapidly saw tremendous growth. Salesforce, for example, positioned itself as a pioneer of this technology and saw great wins. The tides are turning though. As much as cloud providers still proclaim that they’re the most cost-effective and efficient solution for businesses of all sizes, this is increasingly clashing with the day-to-day experience. Cloud Computing was touted as the solution for scalability, flexibility, and reduced operational burdens. Increasingly, though, companies are finding that, at scale, the costs and control limitations outweigh the benefits.​ Attracted by free AWS credits, me and my CTO started out with setting up our entire company IT infrastructure on the cloud. However, we were shocked when we saw the costs ballooning after just a few software tests. We decided to invest in a high-quality server and moved our whole infrastructure onto it. And we’re not looking back: This decision is already saving us hundreds of Euros per month. We’re not the only ones: Dropbox already made this move in 2016 and saved close to $75 million over the ensuing two years. The company behind Basecamp, 37signals, completed this transition in 2022, and expects to save $7 million over five years. We’ll dive deeper into the how and why of this trend and the cost savings that are associated with it. You can expect some practical insights that will help you make or influence such a decision at your company, too. Cloud costs have been exploding According to a recent study by Harness, 21% of enterprise cloud infrastructure spend—which will be equivalent to $44.5 billion in 2025—is wasted on underutilized resources. According to the study author, cloud spend is one of the biggest cost drivers for many software enterprises, second only to salaries. The premise of this study is that developers must develop a keener eye on costs. However, I disagree. Cost control can only get you so far—and many smart developers are already spending inordinate amounts of their time on cost control instead of building actual products. Cloud costs have a tendency to balloon over time: Storage costs per GB of data might seem low, but when you’re dealing with terabytes of data—which even we as a three-person startup are already doing—costs add up very quickly. Add to this retrieval and egress fees, and you’re faced with a bill you cannot unsee. Steep retrieval and egress fees only serve one thing: Cloud providers want to incentivize you to keep as much data as possible on the platform, so they can make money off every operation. If you download data from the cloud, it will cost you inordinate amounts of money. Variable costs based on CPU and GPU usage often spike during high-performance workloads. A report by CNCF found that almost half of Kubernetes adopters found that they’d exceeded their budget as a result. Kubernetes is an open-source container orchestration software that is often used for cloud deployments. The pay-per-use model of the cloud has its advantages, but billing becomes unpredictable as a result. Costs can then explode during usage spikes. Cloud add-ons for security, monitoring, and data analytics also come at a premium, which often increases costs further. As a result, many IT leaders have started migrating back to on-premises servers. A 2023 survey by Uptime found that 33% of respondents had repatriated at least some production applications in the past year. Cloud providers have not restructured their billing in response to this trend. One could argue that doing so would seriously impact their profitability, especially in a largely consolidated market where competitive pressure by upstarts and outsiders is limited. As long as this is the case, the trend towards on-premises is expected to continue. Cost efficiency and control There is a reason that cloud providers tend to advertise so much to small firms and startups. The initial setup costs of a cloud infrastructure are low because of pay-as-you-go models and free credits. The easy setup can be a trap, though, especially once you start scaling. (At my firm, we noticed our costs going out of control even before we scaled to a decent extent, simply because we handle large amounts of data.) Monthly costs for on-premises servers are fixed and predictable; costs for cloud services can quickly balloon beyond expectations. As mentioned before, cloud providers also charge steep data egress fees, which can quickly add up when you’re considering a hybrid infrastructure. Security costs can initially be higher on-premises. On the other hand, you have full control over everything you implement. Cloud providers cover infrastructure security, but you remain responsible for data security and configuration. This often requires paid add-ons. A round-up can be found in the table above. On the whole, an on-premises infrastructure comes with higher setup costs and needs considerable know-how. This initial investment pays off quickly, though, because you tend to have very predictable monthly costs and full control over additions like security measures. There are plenty of prominent examples of companies that have saved millions by moving back on-premises. Whether this is a good choice for you depends on several factors, though, which need to be assessed carefully. Should you move back on-premises? Whether you should make the shift back to server racks depends on several factors. The most important considerations in most cases are financial, operational, and strategic. From a financial point of view, your company’s cash structure plays a big role. If you prefer lean capital expenditures but have no problem racking up high operational costs every month, then you should remain on the cloud. If you can make a higher capital expenditure up front and then refrain from bleeding cash, you should do this though. At the end of the day, the total operational costs (TCO) are key though. If your operational costs on cloud are consistently lower than running servers yourself, then you should absolutely stay on the cloud. From an operational point of view, staying on the cloud can make sense if you often face spikes in usage. On-premises servers can only carry so much traffic; cloud servers scale pretty seamlessly in proportion to demand. If expensive and specialized hardware is more accessible for you on the cloud, this is also a point in favor of staying on the cloud. On the other hand, if you are worried about complying with specific regulations (like GDPR, HIPAA, or CSRD for example), then the shared-responsibility model of cloud services is likely not for you. Strategically speaking, having full control of your infrastructure can be a strategic advantage. It keeps you from getting locked in with a vendor and having to play along with whatever they bill you and what services they are able to offer you. If you plan a geographic expansion or rapidly deploy new services, then cloud can be advantageous though. In the long run, however, going on-premises might make sense even when you’re expanding geographically or in your scope of services, due to increased control and lower operational costs. The decision to move back on-premises depends on several factors. Diagram generated with the help of Claude AI. On the whole, if you value predictability, control, and compliance, you should consider running on-premises. If, on the other hand, you value flexibility, then staying on the cloud might be your better choice. How to repatriate easily If you are considering repatriating your services, here is a brief checklist to follow: Assess Current Cloud Usage: Inventory applications and data volume. Cost Analysis: Calculate current cloud costs vs. projected on-prem costs. Select On-Prem Infrastructure: Servers, storage, and networking requirements. Minimize Data Egress Costs: Use compression and schedule transfers during off-peak hours. Security Planning: Firewalls, encryption, and access controls for on-prem. Test and Migrate: Pilot migration for non-critical workloads first. Monitor and Optimize: Set up monitoring for resources and adjust. Repatriation is not just for enterprise companies that make the headlines. As the example of my firm shows, even small startups need to make this consideration. The earlier you make the migration, the less cash you’ll bleed. The bottom line: Cloud is not dead, but the hype around it is dying Cloud services aren’t going anywhere. They offer flexibility and scalability, which are unmatched for certain use cases. Startups and companies with unpredictable or rapidly growing workloads still benefit greatly from cloud solutions. That being said, even early-stage companies can benefit from on-premises infrastructure, for example if the large data loads they’re handling would make the cloud bill balloon out of control. This was the case at my firm. The cloud has often been marketed as a one-size-fits-all solution for everything from data storage to AI workloads. We can see that this is not the case; the reality is a bit more granular than this. As companies scale, the costs, compliance challenges, and performance limitations of cloud computing become impossible to ignore. The hype around cloud services is dying because experience is showing us that there are real limits and plenty of hidden costs. In addition, cloud providers can often not adequately provide for security solutions, options for compliance, and user control if you don’t pay a hefty premium for all this. Most companies will likely adopt a hybrid approach in the long run: On-premises offers control and predictability; cloud servers can jump into the fray when demand from users spikes. There’s no real one-size-fits-all solution. However, there are specific criteria that should help you guide your decision. Like every hype, there are ebbs and flows. The fact that cloud services are no longer hyped does not mean that you need to go all-in on server racks now. It does, however, invite for a deeper reflection about the advantages that this trend offers for your company.

Ten years ago, everybody was fascinated by the cloud. It was the new thing, and companies that adopted it rapidly saw tremendous growth. Salesforce, for example, positioned itself as a pioneer of this technology and saw great wins.

The tides are turning though. As much as cloud providers still proclaim that they’re the most cost-effective and efficient solution for businesses of all sizes, this is increasingly clashing with the day-to-day experience.

Cloud Computing was touted as the solution for scalability, flexibility, and reduced operational burdens. Increasingly, though, companies are finding that, at scale, the costs and control limitations outweigh the benefits.​

Attracted by free AWS credits, me and my CTO started out with setting up our entire company IT infrastructure on the cloud. However, we were shocked when we saw the costs ballooning after just a few software tests. We decided to invest in a high-quality server and moved our whole infrastructure onto it. And we’re not looking back: This decision is already saving us hundreds of Euros per month.

We’re not the only ones: Dropbox already made this move in 2016 and saved close to $75 million over the ensuing two years. The company behind Basecamp, 37signals, completed this transition in 2022, and expects to save $7 million over five years.

We’ll dive deeper into the how and why of this trend and the cost savings that are associated with it. You can expect some practical insights that will help you make or influence such a decision at your company, too.

Cloud costs have been exploding

According to a recent study by Harness, 21% of enterprise cloud infrastructure spend—which will be equivalent to $44.5 billion in 2025—is wasted on underutilized resources. According to the study author, cloud spend is one of the biggest cost drivers for many software enterprises, second only to salaries.

The premise of this study is that developers must develop a keener eye on costs. However, I disagree. Cost control can only get you so far—and many smart developers are already spending inordinate amounts of their time on cost control instead of building actual products.

Cloud costs have a tendency to balloon over time: Storage costs per GB of data might seem low, but when you’re dealing with terabytes of data—which even we as a three-person startup are already doing—costs add up very quickly. Add to this retrieval and egress fees, and you’re faced with a bill you cannot unsee.

Steep retrieval and egress fees only serve one thing: Cloud providers want to incentivize you to keep as much data as possible on the platform, so they can make money off every operation. If you download data from the cloud, it will cost you inordinate amounts of money.

Variable costs based on CPU and GPU usage often spike during high-performance workloads. A report by CNCF found that almost half of Kubernetes adopters found that they’d exceeded their budget as a result. Kubernetes is an open-source container orchestration software that is often used for cloud deployments.

The pay-per-use model of the cloud has its advantages, but billing becomes unpredictable as a result. Costs can then explode during usage spikes. Cloud add-ons for security, monitoring, and data analytics also come at a premium, which often increases costs further.

As a result, many IT leaders have started migrating back to on-premises servers. A 2023 survey by Uptime found that 33% of respondents had repatriated at least some production applications in the past year.

Cloud providers have not restructured their billing in response to this trend. One could argue that doing so would seriously impact their profitability, especially in a largely consolidated market where competitive pressure by upstarts and outsiders is limited. As long as this is the case, the trend towards on-premises is expected to continue.

Cost efficiency and control

There is a reason that cloud providers tend to advertise so much to small firms and startups. The initial setup costs of a cloud infrastructure are low because of pay-as-you-go models and free credits.

The easy setup can be a trap, though, especially once you start scaling. (At my firm, we noticed our costs going out of control even before we scaled to a decent extent, simply because we handle large amounts of data.) Monthly costs for on-premises servers are fixed and predictable; costs for cloud services can quickly balloon beyond expectations.

As mentioned before, cloud providers also charge steep data egress fees, which can quickly add up when you’re considering a hybrid infrastructure.

Security costs can initially be higher on-premises. On the other hand, you have full control over everything you implement. Cloud providers cover infrastructure security, but you remain responsible for data security and configuration. This often requires paid add-ons.

https://datawrapper.dwcdn.net/czWge/1/

A round-up can be found in the table above. On the whole, an on-premises infrastructure comes with higher setup costs and needs considerable know-how. This initial investment pays off quickly, though, because you tend to have very predictable monthly costs and full control over additions like security measures.

There are plenty of prominent examples of companies that have saved millions by moving back on-premises. Whether this is a good choice for you depends on several factors, though, which need to be assessed carefully.

Should you move back on-premises?

Whether you should make the shift back to server racks depends on several factors. The most important considerations in most cases are financial, operational, and strategic.

From a financial point of view, your company’s cash structure plays a big role. If you prefer lean capital expenditures but have no problem racking up high operational costs every month, then you should remain on the cloud. If you can make a higher capital expenditure up front and then refrain from bleeding cash, you should do this though.

At the end of the day, the total operational costs (TCO) are key though. If your operational costs on cloud are consistently lower than running servers yourself, then you should absolutely stay on the cloud.

From an operational point of view, staying on the cloud can make sense if you often face spikes in usage. On-premises servers can only carry so much traffic; cloud servers scale pretty seamlessly in proportion to demand. If expensive and specialized hardware is more accessible for you on the cloud, this is also a point in favor of staying on the cloud. On the other hand, if you are worried about complying with specific regulations (like GDPR, HIPAA, or CSRD for example), then the shared-responsibility model of cloud services is likely not for you.

Strategically speaking, having full control of your infrastructure can be a strategic advantage. It keeps you from getting locked in with a vendor and having to play along with whatever they bill you and what services they are able to offer you. If you plan a geographic expansion or rapidly deploy new services, then cloud can be advantageous though. In the long run, however, going on-premises might make sense even when you’re expanding geographically or in your scope of services, due to increased control and lower operational costs.

The decision to move back on-premises depends on several factors. Diagram generated with the help of Claude AI.

On the whole, if you value predictability, control, and compliance, you should consider running on-premises. If, on the other hand, you value flexibility, then staying on the cloud might be your better choice.

How to repatriate easily

If you are considering repatriating your services, here is a brief checklist to follow:

  • Assess Current Cloud Usage: Inventory applications and data volume.
  • Cost Analysis: Calculate current cloud costs vs. projected on-prem costs.
  • Select On-Prem Infrastructure: Servers, storage, and networking requirements.
  • Minimize Data Egress Costs: Use compression and schedule transfers during off-peak hours.
  • Security Planning: Firewalls, encryption, and access controls for on-prem.
  • Test and Migrate: Pilot migration for non-critical workloads first.
  • Monitor and Optimize: Set up monitoring for resources and adjust.

Repatriation is not just for enterprise companies that make the headlines. As the example of my firm shows, even small startups need to make this consideration. The earlier you make the migration, the less cash you’ll bleed.

The bottom line: Cloud is not dead, but the hype around it is dying

Cloud services aren’t going anywhere. They offer flexibility and scalability, which are unmatched for certain use cases. Startups and companies with unpredictable or rapidly growing workloads still benefit greatly from cloud solutions.

That being said, even early-stage companies can benefit from on-premises infrastructure, for example if the large data loads they’re handling would make the cloud bill balloon out of control. This was the case at my firm.

The cloud has often been marketed as a one-size-fits-all solution for everything from data storage to AI workloads. We can see that this is not the case; the reality is a bit more granular than this. As companies scale, the costs, compliance challenges, and performance limitations of cloud computing become impossible to ignore.

The hype around cloud services is dying because experience is showing us that there are real limits and plenty of hidden costs. In addition, cloud providers can often not adequately provide for security solutions, options for compliance, and user control if you don’t pay a hefty premium for all this.

Most companies will likely adopt a hybrid approach in the long run: On-premises offers control and predictability; cloud servers can jump into the fray when demand from users spikes.

There’s no real one-size-fits-all solution. However, there are specific criteria that should help you guide your decision. Like every hype, there are ebbs and flows. The fact that cloud services are no longer hyped does not mean that you need to go all-in on server racks now. It does, however, invite for a deeper reflection about the advantages that this trend offers for your company.

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Cisco unveils prototype quantum networking chip

Clock synchronization allows for coordinated time-dependent communications between end points that might be cloud databases or in large global databases that could be sitting across the country or across the world, he said. “We saw recently when we were visiting Lawrence Berkeley Labs where they have all of these data sources such as radio telescopes, optical telescopes, satellites, the James Webb platform. All of these end points are taking snapshots of a piece of space, and they need to synchronize those snapshots to the picosecond level, because you want to detect things like meteorites, something that is moving faster than the rotational speed of planet Earth. So the only way you can detect that quickly is if you synchronize these snapshots at the picosecond level,” Pandey said. For security use cases, the chip can ensure that if an eavesdropper tries to intercept the quantum signals carrying the key, they will likely disturb the state of the qubits, and this disturbance can be detected by the legitimate communicating parties and the link will be dropped, protecting the sender’s data. This feature is typically implemented in a Quantum Key Distribution system. Location information can serve as a critical credential for systems to authenticate control access, Pandey said. The prototype quantum entanglement chip is just part of the research Cisco is doing to accelerate practical quantum computing and the development of future quantum data centers.  The quantum data center that Cisco envisions would have the capability to execute numerous quantum circuits, feature dynamic network interconnection, and utilize various entanglement generation protocols. The idea is to build a network connecting a large number of smaller processors in a controlled environment, the data center warehouse, and provide them as a service to a larger user base, according to Cisco.  The challenges for quantum data center network fabric

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Zyxel launches 100GbE switch for enterprise networks

Port specifications include: 48 SFP28 ports supporting dual-rate 10GbE/25GbE connectivity 8 QSFP28 ports supporting 100GbE connections Console port for direct management access Layer 3 routing capabilities include static routing with support for access control lists (ACLs) and VLAN segmentation. The switch implements IEEE 802.1Q VLAN tagging, port isolation, and port mirroring for traffic analysis. For link aggregation, the switch supports IEEE 802.3ad for increased throughput and redundancy between switches or servers. Target applications and use cases The CX4800-56F targets multiple deployment scenarios where high-capacity backbone connectivity and flexible port configurations are required. “This will be for service providers initially or large deployments where they need a high capacity backbone to deliver a primarily 10G access layer to the end point,” explains Nguyen. “Now with Wi-Fi 7, more 10G/25G capable POE switches are being powered up and need interconnectivity without the bottleneck. We see this for data centers, campus, MDU (Multi-Dwelling Unit) buildings or community deployments.” Management is handled through Zyxel’s NebulaFlex Pro technology, which supports both standalone configuration and cloud management via the Nebula Control Center (NCC). The switch includes a one-year professional pack license providing IGMP technology and network analytics features. The SFP28 ports maintain backward compatibility between 10G and 25G standards, enabling phased migration paths for organizations transitioning between these speeds.

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Engineers rush to master new skills for AI-driven data centers

According to the Uptime Institute survey, 57% of data centers are increasing salary spending. Data center job roles that saw the highest increases were in operations management – 49% of data center operators said they saw highest increases in this category – followed by junior and mid-level operations staff at 45%, and senior management and strategy at 35%. Other job categories that saw salary growth were electrical, at 32% and mechanical, at 23%. Organizations are also paying premiums on top of salaries for particular skills and certifications. Foote Partners tracks pay premiums for more than 1,300 certified and non-certified skills for IT jobs in general. The company doesn’t segment the data based on whether the jobs themselves are data center jobs, but it does track 60 skills and certifications related to data center management, including skills such as storage area networking, LAN, and AIOps, and 24 data center-related certificates from Cisco, Juniper, VMware and other organizations. “Five of the eight data center-related skills recording market value gains in cash pay premiums in the last twelve months are all AI-related skills,” says David Foote, chief analyst at Foote Partners. “In fact, they are all among the highest-paying skills for all 723 non-certified skills we report.” These skills bring in 16% to 22% of base salary, he says. AIOps, for example, saw an 11% increase in market value over the past year, now bringing in a premium of 20% over base salary, according to Foote data. MLOps now brings in a 22% premium. “Again, these AI skills have many uses of which the data center is only one,” Foote adds. The percentage increase in the specific subset of these skills in data centers jobs may vary. The Uptime Institute survey suggests that the higher pay is motivating workers to stay in the

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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