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Anthropic’s chief scientist on 5 ways agents will be even better in 2025

Agents are the hottest thing in tech right now. Top firms from Google DeepMind to OpenAI to Anthropic are racing to augment large language models with the ability to carry out tasks by themselves. Known as agentic AI in industry jargon, such systems have fast become the new target of Silicon Valley buzz. Everyone from Nvidia to Salesforce is talking about how they are going to upend the industry.  “We believe that, in 2025, we may see the first AI agents ‘join the workforce’ and materially change the output of companies,” Sam Altman claimed in a blog post last week. In the broadest sense, an agent is a software system that goes off and does something, often with minimal to zero supervision. The more complex that thing is, the smarter the agent needs to be. For many, large language models are now smart enough to power agents that can do a whole range of useful tasks for us, such as filling out forms, looking up a recipe and adding the ingredients to an online grocery basket, or using a search engine to do last-minute research before a meeting and producing a quick bullet-point summary. In October, Anthropic showed off one of the most advanced agents yet: an extension of its Claude large language model called computer use. As the name suggests, it lets you direct Claude to use a computer much as a person would, by moving a cursor, clicking buttons, and typing text. Instead of simply having a conversation with Claude, you can now ask it to carry out on-screen tasks for you. Anthropic notes that the feature is still cumbersome and error-prone. But it is already available to a handful of testers, including third-party developers at companies such as DoorDash, Canva, and Asana. Computer use is a glimpse of what’s to come for agents. To learn what’s coming next, MIT Technology Review talked to Anthropic’s cofounder and chief scientist Jared Kaplan. Here are five ways that agents are going to get even better in 2025. (Kaplan’s answers have been lightly edited for length and clarity.) 1/ Agents will get better at using tools “I think there are two axes for thinking about what AI is capable of. One is a question of how complex the task is that a system can do. And as AI systems get smarter, they’re getting better in that direction. But another direction that’s very relevant is what kinds of environments or tools the AI can use.  “So, like, if you go back almost 10 years now to [DeepMind’s Go-playing model] AlphaGo, we had AI systems that were superhuman in terms of how well they could play board games. But if all you can work with is a board game, then that’s a very restrictive environment. It’s not actually useful, even if it’s very smart. With text models, and then multimodal models, and now computer use—and perhaps in the future with robotics—you’re moving toward bringing AI into different situations and tasks, and making it useful.  “We were excited about computer use basically for that reason. Until recently, with large language models, it’s been necessary to give them a very specific prompt, give them very specific tools, and then they’re restricted to a specific kind of environment. What I see is that computer use will probably improve quickly in terms of how well models can do different tasks and more complex tasks. And also to realize when they’ve made mistakes, or realize when there’s a high-stakes question and it needs to ask the user for feedback.” 2/ Agents will understand context   “Claude needs to learn enough about your particular situation and the constraints that you operate under to be useful. Things like what particular role you’re in, what styles of writing or what needs you and your organization have. “I think that we’ll see improvements there where Claude will be able to search through things like your documents, your Slack, etc., and really learn what’s useful for you. That’s underemphasized a bit with agents. It’s necessary for systems to be not only useful but also safe, doing what you expected. “Another thing is that a lot of tasks won’t require Claude to do much reasoning. You don’t need to sit and think for hours before opening Google Docs or something. And so I think that a lot of what we’ll see is not just more reasoning but the application of reasoning when it’s really useful and important, but also not wasting time when it’s not necessary.” 3/ Agents will make coding assistants better “We wanted to get a very initial beta of computer use out to developers to get feedback while the system was relatively primitive. But as these systems get better, they might be more widely used and really collaborate with you on different activities. “I think DoorDash, the Browser Company, and Canva are all experimenting with, like, different kinds of browser interactions and designing them with the help of AI. “My expectation is that we’ll also see further improvements to coding assistants. That’s something that’s been very exciting for developers. There’s just a ton of interest in using Claude 3.5 for coding, where it’s not just autocomplete like it was a couple of years ago. It’s really understanding what’s wrong with code, debugging it—running the code, seeing what happens, and fixing it.” 4/ Agents will need to be made safe “We founded Anthropic because we expected AI to progress very quickly and [thought] that, inevitably, safety concerns were going to be relevant. And I think that’s just going to become more and more visceral this year, because I think these agents are going to become more and more integrated into the work we do. We need to be ready for the challenges, like prompt injection.  [Prompt injection is an attack in which a malicious prompt is passed to a large language model in ways that its developers did not foresee or intend. One way to do this is to add the prompt to websites that models might visit.] “Prompt injection is probably one of the No.1 things we’re thinking about in terms of, like, broader usage of agents. I think it’s especially important for computer use, and it’s something we’re working on very actively, because if computer use is deployed at large scale, then there could be, like, pernicious websites or something that try to convince Claude to do something that it shouldn’t do. “And with more advanced models, there’s just more risk. We have a robust scaling policy where, as AI systems become sufficiently capable, we feel like we need to be able to really prevent them from being misused. For example, if they could help terrorists—that kind of thing. “So I’m really excited about how AI will be useful—it’s actually also accelerating us a lot internally at Anthropic, with people using Claude in all kinds of ways, especially with coding. But, yeah, there’ll be a lot of challenges as well. It’ll be an interesting year.”

Agents are the hottest thing in tech right now. Top firms from Google DeepMind to OpenAI to Anthropic are racing to augment large language models with the ability to carry out tasks by themselves. Known as agentic AI in industry jargon, such systems have fast become the new target of Silicon Valley buzz. Everyone from Nvidia to Salesforce is talking about how they are going to upend the industry. 

“We believe that, in 2025, we may see the first AI agents ‘join the workforce’ and materially change the output of companies,” Sam Altman claimed in a blog post last week.

In the broadest sense, an agent is a software system that goes off and does something, often with minimal to zero supervision. The more complex that thing is, the smarter the agent needs to be. For many, large language models are now smart enough to power agents that can do a whole range of useful tasks for us, such as filling out forms, looking up a recipe and adding the ingredients to an online grocery basket, or using a search engine to do last-minute research before a meeting and producing a quick bullet-point summary.

In October, Anthropic showed off one of the most advanced agents yet: an extension of its Claude large language model called computer use. As the name suggests, it lets you direct Claude to use a computer much as a person would, by moving a cursor, clicking buttons, and typing text. Instead of simply having a conversation with Claude, you can now ask it to carry out on-screen tasks for you.

Anthropic notes that the feature is still cumbersome and error-prone. But it is already available to a handful of testers, including third-party developers at companies such as DoorDash, Canva, and Asana.

Computer use is a glimpse of what’s to come for agents. To learn what’s coming next, MIT Technology Review talked to Anthropic’s cofounder and chief scientist Jared Kaplan. Here are five ways that agents are going to get even better in 2025.

(Kaplan’s answers have been lightly edited for length and clarity.)

1/ Agents will get better at using tools

“I think there are two axes for thinking about what AI is capable of. One is a question of how complex the task is that a system can do. And as AI systems get smarter, they’re getting better in that direction. But another direction that’s very relevant is what kinds of environments or tools the AI can use. 

“So, like, if you go back almost 10 years now to [DeepMind’s Go-playing model] AlphaGo, we had AI systems that were superhuman in terms of how well they could play board games. But if all you can work with is a board game, then that’s a very restrictive environment. It’s not actually useful, even if it’s very smart. With text models, and then multimodal models, and now computer use—and perhaps in the future with robotics—you’re moving toward bringing AI into different situations and tasks, and making it useful. 

“We were excited about computer use basically for that reason. Until recently, with large language models, it’s been necessary to give them a very specific prompt, give them very specific tools, and then they’re restricted to a specific kind of environment. What I see is that computer use will probably improve quickly in terms of how well models can do different tasks and more complex tasks. And also to realize when they’ve made mistakes, or realize when there’s a high-stakes question and it needs to ask the user for feedback.”

2/ Agents will understand context  

“Claude needs to learn enough about your particular situation and the constraints that you operate under to be useful. Things like what particular role you’re in, what styles of writing or what needs you and your organization have.

Jared Kaplan

“I think that we’ll see improvements there where Claude will be able to search through things like your documents, your Slack, etc., and really learn what’s useful for you. That’s underemphasized a bit with agents. It’s necessary for systems to be not only useful but also safe, doing what you expected.

“Another thing is that a lot of tasks won’t require Claude to do much reasoning. You don’t need to sit and think for hours before opening Google Docs or something. And so I think that a lot of what we’ll see is not just more reasoning but the application of reasoning when it’s really useful and important, but also not wasting time when it’s not necessary.”

3/ Agents will make coding assistants better

“We wanted to get a very initial beta of computer use out to developers to get feedback while the system was relatively primitive. But as these systems get better, they might be more widely used and really collaborate with you on different activities.

“I think DoorDash, the Browser Company, and Canva are all experimenting with, like, different kinds of browser interactions and designing them with the help of AI.

“My expectation is that we’ll also see further improvements to coding assistants. That’s something that’s been very exciting for developers. There’s just a ton of interest in using Claude 3.5 for coding, where it’s not just autocomplete like it was a couple of years ago. It’s really understanding what’s wrong with code, debugging it—running the code, seeing what happens, and fixing it.”

4/ Agents will need to be made safe

“We founded Anthropic because we expected AI to progress very quickly and [thought] that, inevitably, safety concerns were going to be relevant. And I think that’s just going to become more and more visceral this year, because I think these agents are going to become more and more integrated into the work we do. We need to be ready for the challenges, like prompt injection. 

[Prompt injection is an attack in which a malicious prompt is passed to a large language model in ways that its developers did not foresee or intend. One way to do this is to add the prompt to websites that models might visit.]

“Prompt injection is probably one of the No.1 things we’re thinking about in terms of, like, broader usage of agents. I think it’s especially important for computer use, and it’s something we’re working on very actively, because if computer use is deployed at large scale, then there could be, like, pernicious websites or something that try to convince Claude to do something that it shouldn’t do.

“And with more advanced models, there’s just more risk. We have a robust scaling policy where, as AI systems become sufficiently capable, we feel like we need to be able to really prevent them from being misused. For example, if they could help terrorists—that kind of thing.

“So I’m really excited about how AI will be useful—it’s actually also accelerating us a lot internally at Anthropic, with people using Claude in all kinds of ways, especially with coding. But, yeah, there’ll be a lot of challenges as well. It’ll be an interesting year.”

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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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