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The rise of browser-use agents: Why Convergence’s Proxy is beating OpenAI’s Operator

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new wave of AI-powered browser-use agents is emerging, promising to transform how enterprises interact with the web. These agents can autonomously navigate websites, retrieve information, and even complete transactions – but early testing reveals significant […]

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A new wave of AI-powered browser-use agents is emerging, promising to transform how enterprises interact with the web. These agents can autonomously navigate websites, retrieve information, and even complete transactions – but early testing reveals significant gaps between promise and performance.

While consumer examples offered by OpenAI’s new browser-use agent Operator, like ordering pizza or buying game tickets, have grabbed headlines, the question is about where the main developer and enterprise use cases are. “The thing that we don’t know is what will be the killer app,” said Sam Witteveen, co-founder of Red Dragon, a company that develops AI agent applications. “My guess is it’s going to be things that just take time on the web that you don’t actually enjoy.” This includes things like going on the web and searching for the cheapest price of a product or booking the best hotel accommodations. More likely it will be used in combination with other tools like Deep Research, where companies can then do even more sophisticated research plus execution of tasks around the web.

Companies need to carefully evaluate the rapidly evolving landscape as established players and startups take different approaches to solving the autonomous browsing challenge.

Key players in the browser-use agent landscape

The field has quickly become crowded with both major tech companies and innovative startups:

Operator and Proxy are the most advanced, in terms of being consumer-friendly and out-of-the-box ready. Many of the others appear to be positioning themselves more for developer or enterprise usage. For example, Browser Use, a Y-Combinator startup that allows users to customize the models used with the agent. This gives you more control over how the agent works, including using a model from your local machine. But it’s definitely more involved.

The others listed above provide a varying degree of functionality and interaction with local machine resources. I decided not even to test ByteDance’s UI-TARS for now, because it requested lower level access to my machine’s security and privacy features (if I test it out, I’ll definitely use a secondary computer). 

Testing reveals reasoning challenges

So the easiest to test are OpenAI’s Operator and Convergence’s Proxy. In our testing, the results highlighted how reasoning capabilities can matter more than raw automation features. Operator, in particular, was more buggy.

For example, I asked the agents to find and summarize VentureBeat’s five most popular stories. It was an ambiguous task, because VentureBeat doesn’t have a “most popular” section per se. Operator struggled with this. It first fell into an infinite scrolling loop while searching for ‘most popular’ stories, requiring manual intervention. In another attempt, it found a three-year-old article titled “Top five stories of the week.” In contrast, Proxy demonstrated better reasoning by identifying the five most visible stories on the homepage as a practical proxy for popularity, and it gave accurate summaries.

The distinction became even clearer in real-world tasks. I asked the agents to book a reservation at a romantic restaurant for noon in Napa, California. Operator approached the task linearly — finding a romantic restaurant first, then checking availability at noon. When no tables were available, it reached a dead end. Proxy showed more sophisticated reasoning by starting with OpenTable to find restaurants that were both romantic and available at the desired time. It even came back with a slightly better rated restaurant.

Even seemingly simple tasks revealed important differences. When searching for a “YubiKey 5C NFC price” on Amazon, Proxy quickly found the item more easily than Operator. 

OpenAI hasn’t divulged much about technologies it uses for training its Operator agent, other than saying it has trained its model on browser-use tasks. Convergence, however, has provided more detail: Its agent uses something called Generative Tree Search to “leverage Web-World Models that predict the state of the web after a proposed action has been taken. These are generated recursively to produce a tree of possible futures that are searched over to select the next optimal action, as ranked by our value models. Our Web-World models can also be used to train agents in hypothetical situations without generating a lot of expensive data.” (More here).

Benchmarks may be useless for now

On paper, these tools appear closely matched. Convergence’s Proxy achieves 88% on the WebVoyager benchmark, which evaluates web agents across 643 real-world tasks on 15 popular websites like Amazon and Booking.com. OpenAI’s Operator scores 87%, while Browser-Use says it reaches 89% but only after changing the WebVoyager codebase slightly, it conceded, “according to our needs”.

These benchmark scores should really be taken with a grain of salt, though, as they can be gamed. The real test comes in practical usage for real-world cases. It’s very early, the space is so rapidly changing, and these products are changing almost on a daily basis. The results will depend more on the specific jobs you’re trying to do, and you may want to instead rely on the vibes you get while using the different products.

Enterprise implications

The implications for enterprise automation are significant. As Witteveen points out in our video podcast conversation about this, where we do a deep dive into this browser-use trend, many companies are currently paying for virtual assistants – operated by real people – to handle basic web research and data gathering tasks. These browser-use agents could dramatically change that equation.

“If AI takes this over,” Witteveen notes, “that’s going to be some of the first low hanging fruit of people losing their jobs. It’s going to show up in some of these kinds of things.”

This could feed into the robotic process automation (RPA) trend, where browser use is pulled in as just another tool for companies to automate more tasks. And as mentioned earlier, the more powerful uses cases will be when an agent combined browser use with other tools, including things like Deep Research, where an LLM-driven agent uses a search tool plus browser use to do more sophisticated jobs.

Cost dynamics driving innovation

Another key factor driving rapid development is the availability of powerful open-source reasoning models like DeepSeek-R1. This allows companies building these browser-use agents to compete effectively with larger players by leveraging these models rather than building their own.

The pricing pressure is already evident. While OpenAI requires a $200 monthly ChatGPT Pro subscription to access Operator, Convergence offers limited free use (up to five uses per day) and a $20/month unlimited plan. This competitive dynamic should accelerate enterprise adoption, though clear use cases are still emerging.

Security and integration challenges

Several hurdles remain before widespread enterprise adoption. Some websites actively block automated browsing, while others require CAPTCHA verification. While OpenAI and Convergence have tools that can get past CAPTCHAs, they let users take over the task to fill them out — instead of doing them directly, since the whole point of CAPTCHAs is to ensure a human is at the other end. Tools like ByteDance’s UI-TARS request deep system access, which raises security concerns for enterprise deployment.

Additionally, the approach to website cooperation varies. OpenAI has worked with specific partners like Instacart, Priceline, DoorDash and Etsy, while others attempt to navigate any website. This inconsistency could impact reliability for enterprise use cases. And of course, any time an agent hits a site requiring login details, that will slow things — as the agents will turn things over to you to fill in those details.

Looking ahead

For enterprises evaluating these tools, the focus should be on specific use cases where autonomous web interaction could provide clear value – whether in research, customer service, or process automation. The technology is progressing rapidly, but success will depend on matching capabilities to concrete business needs.

As this space evolves, expect to see more enterprise-focused features and potentially specialized agents for specific industries or tasks. The race between established players and innovative startups should drive both technical advancement and competitive pricing, making 2025 a crucial year for enterprise browser-use agent adoption.

For more detail on these trends and testing results, check out the full video conversation between Sam Witteveen and myself.

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Neoclouds gain momentum in a supply-constrained world

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What is AI networking? How it adds intelligence to your infrastructure

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Aria Networks raises $125M and debuts its approach for AI-optimized networks

That embedded telemetry feeds adaptive tuning of Dynamic Load Balancing parameters, Data Center Quantized Congestion Notification (DCQCN) and failover logic without waiting for a threshold breach or a manual intervention. The platform architecture is layered. At the lowest levels, agents react in microseconds to link-level events such as transceiver flaps, rerouting leaf-spine traffic in milliseconds. At higher layers, agents make more strategic decisions about flow placement across the cluster. At the cloud layer, a large language model-based agent surfaces correlated insights to operators in natural language, allowing them to ask questions about specific jobs or alert conditions and receive context-aware responses. Karam argued that simply bolting an LLM onto an existing architecture does not deliver the same result. “If you ask it to do anything, it could hallucinate and bring down the network,” he said. “It doesn’t have any of the context or the data that’s required for this approach to be made safe.” Aria also exposes an MCP server, allowing external systems such as job schedulers and LLM routers to query network state directly and integrate it into their own decision-making. MFU and token efficiency as the target metrics Traditional networking is often evaluated in terms of bandwidth and latency. Aria is centering its platform around two metrics: Model FLOPS Utilization (MFU) and token efficiency. MFU is defined as the ratio of achieved FLOPS per accelerator to the theoretical peak. In practice, Karam said, MFU for training workloads typically runs between 33% and 45%, and inference often comes in below 30%. “The network has a major impact on the MFU, and therefore the token efficiency, because the network touches every aspect, every other component in your cluster,” Karam said.

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New v2 UALink specification aims to catch up to NVLink

But given there are no products currently available using UALink 1.0, UALink 2.0 might be viewed as a premature launch Need to play catch up David Harold, senior analyst with Jon Peddie Research, was guarded in his reaction. “While 2.0 is a significant step forward from 1.0, we need to bear in mind that even 1.0 solutions aren’t shipping yet – they aren’t due until later this year. So, Nvidia is way ahead of the open alternatives on connectivity, indeed ahead of the proprietary or Ethernet based solutions too,” he said. What this means, he added, is that non-Nvidia alternatives are currently lagging in the market. “They need to play catch up on several fronts, not just networking. … I can’t think of a single shipping product that meaningfully has advantages over a Nvidia solution,” he said. “Ultimately UALink remains desirable since it will enable heterogeneous, multi-vendor environments but it’s quite a way behind NVLink today. ” There are plenty of signs that organizations will find it hard to break free of the Nvidia dominance, however. A couple of months ago, RISC-V pioneer SiFive signed a deal with Nvidia to incorporate Nvidia NVLink Fusion into its data center products, a departure for RISC companies. According to Harold, other companies could be joining it. “Custom ASIC company MediaTek is an NVLink partner, and they told me last week that they are planning to integrate it directly into next-generation custom silicon for AI applications,” he said. “This will enable a wider range of companies to use NVLink as their high-speed interconnect.” Other options And, Harold noted, Nvidia is already looking at other options. “Nvidia is now shifting to look at the copper limit for networking speed, with an interest in using optical connectivity instead,” said Harold.

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Nvidia’s SchedMD acquisition puts open-source AI scheduling under scrutiny

Is the concern valid? Dr. Danish Faruqui, CEO of Fab Economics, a US-based AI hardware and datacenter advisory, said the risk was real. “The skepticism that Nvidia may prioritize its own hardware in future software updates, potentially delaying or under-optimizing support for rivals, is a feasible outcome,” he said. As the primary developer, Nvidia now controls Slurm’s official development roadmap and code review process, Faruqui said, “which could influence how quickly competing chips are integrated on new development or continuous improvement elements.” Owning the control plane alongside GPUs and networking infrastructure such as InfiniBand, he added, allows Nvidia to create a tightly vertically integrated stack that can lead to what he described as “shallow moats, where advanced features are only available or performant on Nvidia hardware.” One concrete test of that, industry observers say, will be how quickly Nvidia integrates support for AMD’s next-generation chips into Slurm’s codebase compared with how quickly it integrates its own forthcoming hardware and networking technologies, such as InfiniBand. Does the Bright Computing precedent hold? Analysts point to Nvidia’s 2022 acquisition of Bright Computing as a reference point, saying the software became optimized for Nvidia chips in ways that disadvantaged users of competing hardware. Nvidia disputed that characterization, saying Bright Computing supports “nearly any CPU or GPU-accelerated cluster.” Rawat said the comparison was instructive but imperfect. “Nvidia’s acquisition of Bright Computing highlights its preference for vertical integration, embedding Bright tightly into DGX and AI Factory stacks rather than maintaining a neutral, multi-vendor orchestration role,” he said. “This reflects a broader strategic pattern — Nvidia seeks to control the full-stack AI infrastructure experience.”

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Two New England states say no to new data centers

It’s getting harder and harder for governments to ignore the impact that data centers are having on their communities, consuming vast amounts of water and driving up electricity prices, experts say. According to a Pew Research Center analysis, data centers consumed 183 terawatt-hours of electricity in 2024, more than 4% of total U.S. electricity use. That demand is projected to more than double to 426 terawatt-hours by 2030. The impact is significant. In 2023, data centers consumed about 26% of Virginia’s electricity supply, although Virginia is notable for having an extremely dense collection of data centers. Alan Howard, senior analyst for infrastructure at Omdia, says he is not surprised at all. “The amount of national press coverage regarding what is arguably a limited number of data center ‘horror’ stories has many jurisdictions and states spooked over the potential impacts data center projects might have,” he said. It’s an evolution that’s been coming for some time whereby local legislators have embraced the idea that they don’t want to learn the hard way as others already have, he argues. “All that said, it seems unlikely that there will be broad bans on data center development that would cripple the industry. There’s lots of places to go in the U.S. and developers have warmed up to siting projects in places amenable to their needs, although not ideally convenient,” said Howard.

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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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