OpenAI has upped its lobbying efforts nearly sevenfold

OpenAI spent $1.76 million on government lobbying in 2024 and $510,000 in the last three months of the year alone, according to a new disclosure filed on Tuesday—a significant jump from 2023, when the company spent just $260,000 on Capitol Hill. The company also disclosed a new in-house lobbyist, Meghan Dorn, who worked for five years for Senator Lindsey Graham and started at OpenAI in October. The filing also shows activity related to two new pieces of legislation in the final months of the year: the House’s AI Advancement and Reliability Act, which would set up a government center for AI research, and the Senate’s Future of Artificial Intelligence Innovation Act, which would create shared benchmark tests for AI models. 

OpenAI did not respond to questions about its lobbying efforts.

But perhaps more important, the disclosure is a clear signal of the company’s arrival as a political player, as its first year of serious lobbying ends and Republican control of Washington begins. While OpenAI’s lobbying spending is still dwarfed by its peers’—Meta tops the list of Big Tech spenders, with more than $24 million in 2024—the uptick comes as it and other AI companies have helped redraw the shape of AI policy. 

For the past few years, AI policy has been something like a whack-a-mole response to the risks posed by deepfakes and misinformation. But over the last year, AI companies have started to position the success of the technology as pivotal to national security and American competitiveness, arguing that the government must therefore support the industry’s growth. As a result, OpenAI and others now seem poised to gain access to cheaper energy, lucrative national security contracts, and a more lax regulatory environment that’s unconcerned with the minutiae of AI safety.

While the big players seem more or less aligned on this grand narrative, messy divides on other issues are still threatening to break through the harmony on display at President Trump’s inauguration this week.

AI regulation really began in earnest after ChatGPT launched in November 2022. At that point, “a lot of the conversation was about responsibility,” says Liana Keesing, campaigns manager for technology reform at Issue One, a democracy nonprofit that tracks Big Tech’s influence. 

Companies were asked what they’d do about sexually abusive deepfake images and election disinformation. “Sam Altman did a very good job coming in and painting himself early as a supporter of that process,” Keesing says. 

OpenAI started its official lobbying effort around October 2023, hiring Chan Park—a onetime Senate Judiciary Committee counsel and Microsoft lobbyist—to lead the effort. Lawmakers, particularly then Senate majority leader Chuck Schumer, were vocal about wanting to curb these particular harms; OpenAI hired Schumer’s former legal counsel, Reginald Babin, as a lobbyist, according to data from OpenSecrets. This past summer, the company hired the veteran political operative Chris Lehane as its head of global policy. 

OpenAI’s previous disclosures confirm that the company’s lobbyists subsequently focused much of last year on legislation like the No Fakes Act and the Protect Elections from Deceptive AI Act. The bills did not materialize into law. But as the year went on, the regulatory goals of AI companies began to change. “One of the biggest shifts that we’ve seen,” Keesing says, “is that they’ve really started to focus on energy.” 

In September, Altman, along with leaders from Nvidia, Anthropic, and Google, visited the White House and pitched the vision that US competitiveness in AI will depend on subsidized energy infrastructure to train the best models. Altman proposed to the Biden administration the construction of multiple five-gigawatt data centers, which would each consume as much electricity as New York City. 

Around the same time, companies like Meta and Microsoft started to say that nuclear energy will provide the path forward for AI, announcing deals aimed at firing up new nuclear power plants. 

It seems likely OpenAI’s policy team was already planning for this particular shift. In April, the company hired lobbyist Matthew Rimkunas, who worked for Bill Gates’s sustainable energy effort Breakthrough Energies and, before that, spent 16 years working for Senator Graham; the South Carolina Republican serves on the Senate subcommittee that manages nuclear safety. 

This new AI energy race is inseparable from the positioning of AI as essential for national security and US competitiveness with China. OpenAI laid out its position in a blog post in October, writing, “AI is a transformational technology that can be used to strengthen democratic values or to undermine them. That’s why we believe democracies should continue to take the lead in AI development.” Then in December, the company went a step further and reversed its policy against working with the military, announcing it would develop AI models with the defense-tech company Anduril to help take down drones around military bases. 

That same month, Sam Altman said during an interview with The Free Press that the Biden administration was “not that effective” in shepherding AI: “The things that I think should have been the administration’s priorities, and I hope will be the next administration’s priorities, are building out massive AI infrastructure in the US, having a supply chain in the US, things like that.”

That characterization glosses over the CHIPS Act, a $52 billion stimulus to the domestic chips industry that is, at least on paper, aligned with Altman’s vision. (It also preceded an executive order Biden issued just last week, to lease federal land to host the type of gigawatt-scale data centers that Altman had been asking for.)

Intentionally or not, Altman’s posture aligned him with the growing camaraderie between President Trump and Silicon Valley. Mark Zuckerberg, Elon Musk, Jeff Bezos, and Sundar Pichai all sat directly behind Trump’s family at the inauguration on Monday, and Altman also attended. Many of them had also made sizable donations to Trump’s inaugural fund, with Altman personally throwing in $1 million.

It’s easy to view the inauguration as evidence that these tech leaders are aligned with each other, and with other players in Trump’s orbit. But there are still some key dividing lines that will be worth watching. Notably, there’s the clash over H-1B visas, which allow many noncitizen AI researchers to work in the US. Musk and Vivek Ramaswamy (who is, as of this week, no longer a part of the so-called Department of Government Efficiency) have been pushing for that visa program to be expanded. This sparked backlash from some allies of the Trump administration, perhaps most loudly Steve Bannon. 

Another fault line is the battle between open- and closed-source AI. Google and OpenAI prevent anyone from knowing exactly what’s in their most powerful models, often arguing that this keeps them from being used improperly by bad actors. Musk has sued OpenAI and Microsoft over the issue, alleging that closed-source models are antithetical to OpenAI’s hybrid nonprofit structure. Meta, whose Llama model is open-source, recently sided with Musk in that lawsuit. Venture capitalist and Trump ally Marc Andreessen echoed these criticisms of OpenAI on X just hours after the inauguration. (Andreessen has also said that making AI models open-source “makes overbearing regulations unnecessary.”) 

Finally, there are the battles over bias and free speech. The vastly different approaches that social media companies have taken to moderating content—including Meta’s recent announcement that it would end its US fact-checking program—raise questions about whether the way AI models are moderated will continue to splinter too. Musk has lamented what he calls the “wokeness” of many leading models, and Andreessen said on Tuesday that “Chinese LLMs are much less censored than American LLMs” (though that’s not quite true, given that many Chinese AI models have government-mandated censorship in place that forbids particular topics). Altman has been more equivocal: “No two people are ever going to agree that one system is perfectly unbiased,” he told The Free Press.

It’s only the start of a new era in Washington, but the White House has been busy. It has repealed many executive orders signed by President Biden, including the landmark order on AI that imposed rules for government use of the technology (while it appears to have kept Biden’s order on leasing land for more data centers). Altman is busy as well. OpenAI, Oracle, and SoftBank reportedly plan to spend up to $500 billion on a joint venture for new data centers; the project was announced by President Trump, with Altman standing alongside. And according to Axios, Altman will also be part of a closed-door briefing with government officials on January 30, reportedly about OpenAI’s development of a powerful new AI agent.

The second wave of AI coding is here

Ask people building generative AI what generative AI is good for right now—what they’re really fired up about—and many will tell you: coding. 

“That’s something that’s been very exciting for developers,” Jared Kaplan, chief scientist at Anthropic, told MIT Technology Review this month: “It’s really understanding what’s wrong with code, debugging it.”

Copilot, a tool built on top of OpenAI’s large language models and launched by Microsoft-backed GitHub in 2022, is now used by millions of developers around the world. Millions more turn to general-purpose chatbots like Anthropic’s Claude, OpenAI’s ChatGPT, and Google DeepMind’s Gemini for everyday help.

“Today, more than a quarter of all new code at Google is generated by AI, then reviewed and accepted by engineers,” Alphabet CEO Sundar Pichai claimed on an earnings call in October: “This helps our engineers do more and move faster.” Expect other tech companies to catch up, if they haven’t already.

It’s not just the big beasts rolling out AI coding tools. A bunch of new startups have entered this buzzy market too. Newcomers such as Zencoder, Merly, Cosine, Tessl (valued at $750 million within months of being set up), and Poolside (valued at $3 billion before it even released a product) are all jostling for their slice of the pie. “It actually looks like developers are willing to pay for copilots,” says Nathan Benaich, an analyst at investment firm Air Street Capital: “And so code is one of the easiest ways to monetize AI.”

Such companies promise to take generative coding assistants to the next level. Instead of providing developers with a kind of supercharged autocomplete, like most existing tools, this next generation can prototype, test, and debug code for you. The upshot is that developers could essentially turn into managers, who may spend more time reviewing and correcting code written by a model than writing it from scratch themselves. 

But there’s more. Many of the people building generative coding assistants think that they could be a fast track to artificial general intelligence (AGI), the hypothetical superhuman technology that a number of top firms claim to have in their sights.

“The first time we will see a massively economically valuable activity to have reached human-level capabilities will be in software development,” says Eiso Kant, CEO and cofounder of Poolside. (OpenAI has already boasted that its latest o3 model beat the company’s own chief scientist in a competitive coding challenge.)

Welcome to the second wave of AI coding. 

Correct code 

Software engineers talk about two types of correctness. There’s the sense in which a program’s syntax (its grammar) is correct—meaning all the words, numbers, and mathematical operators are in the right place. This matters a lot more than grammatical correctness in natural language. Get one tiny thing wrong in thousands of lines of code and none of it will run.

The first generation of coding assistants are now pretty good at producing code that’s correct in this sense. Trained on billions of pieces of code, they have assimilated the surface-level structures of many types of programs.  

But there’s also the sense in which a program’s function is correct: Sure, it runs, but does it actually do what you wanted it to? It’s that second level of correctness that the new wave of generative coding assistants are aiming for—and this is what will really change the way software is made.

“Large language models can write code that compiles, but they may not always write the program that you wanted,” says Alistair Pullen, a cofounder of Cosine. “To do that, you need to re-create the thought processes that a human coder would have gone through to get that end result.”

The problem is that the data most coding assistants have been trained on—the billions of pieces of code taken from online repositories—doesn’t capture those thought processes. It represents a finished product, not what went into making it. “There’s a lot of code out there,” says Kant. “But that data doesn’t represent software development.”

What Pullen, Kant, and others are finding is that to build a model that does a lot more than autocomplete—one that can come up with useful programs, test them, and fix bugs—you need to show it a lot more than just code. You need to show it how that code was put together.  

In short, companies like Cosine and Poolside are building models that don’t just mimic what good code looks like—whether it works well or not—but mimic the process that produces such code in the first place. Get it right and the models will come up with far better code and far better bug fixes. 

Breadcrumbs

But you first need a data set that captures that process—the steps that a human developer might take when writing code. Think of these steps as a breadcrumb trail that a machine could follow to produce a similar piece of code itself.

Part of that is working out what materials to draw from: Which sections of the existing codebase are needed for a given programming task? “Context is critical,” says Zencoder founder Andrew Filev. “The first generation of tools did a very poor job on the context, they would basically just look at your open tabs. But your repo [code repository] might have 5000 files and they’d miss most of it.”

Zencoder has hired a bunch of search engine veterans to help it build a tool that can analyze large codebases and figure out what is and isn’t relevant. This detailed context reduces hallucinations and improves the quality of code that large language models can produce, says Filev: “We call it repo grokking.”

Cosine also thinks context is key. But it draws on that context to create a new kind of data set. The company has asked dozens of coders to record what they were doing as they worked through hundreds of different programming tasks. “We asked them to write down everything,” says Pullen: “Why did you open that file? Why did you scroll halfway through? Why did you close it?” They also asked coders to annotate finished pieces of code, marking up sections that would have required knowledge of other pieces of code or specific documentation to write.

Cosine then takes all that information and generates a large synthetic data set that maps the typical steps coders take, and the sources of information they draw on, to finished pieces of code. They use this data set to train a model to figure out what breadcrumb trail it might need to follow to produce a particular program, and then how to follow it.  

Poolside, based in San Francisco, is also creating a synthetic data set that captures the process of coding, but it leans more on a technique called RLCE—reinforcement learning from code execution. (Cosine uses this too, but to a lesser degree.)

RLCE is analogous to the technique used to make chatbots like ChatGPT slick conversationalists, known as RLHF—reinforcement learning from human feedback. With RLHF, a model is trained to produce text that’s more like the kind human testers say they favor. With RLCE, a model is trained to produce code that’s more like the kind that does what it is supposed to do when it is run (or executed).  

Gaming the system

Cosine and Poolside both say they are inspired by the approach DeepMind took with its game-playing model AlphaZero. AlphaZero was given the steps it could take—the moves in a game—and then left to play against itself over and over again, figuring out via trial and error what sequence of moves were winning moves and which were not.  

“They let it explore moves at every possible turn, simulate as many games as you can throw compute at—that led all the way to beating Lee Sedol,” says Pengming Wang, a founding scientist at Poolside, referring to the Korean Go grandmaster that AlphaZero beat in 2016. Before Poolside, Wang worked at Google DeepMind on applications of AlphaZero beyond board games, including FunSearch, a version trained to solve advanced math problems.

When that AlphaZero approach is applied to coding, the steps involved in producing a piece of code—the breadcrumbs—become the available moves in a game, and a correct program becomes winning that game. Left to play by itself, a model can improve far faster than a human could. “A human coder tries and fails one failure at a time,” says Kant. “Models can try things 100 times at once.”

A key difference between Cosine and Poolside is that Cosine is using a custom version of GPT-4o provided by OpenAI, which makes it possible to train on a larger data set than the base model can cope with, but Poolside is building its own large language model from scratch.

Poolside’s Kant thinks that training a model on code from the start will give better results than adapting an existing model that has sucked up not only billions of pieces of code but most of the internet. “I’m perfectly fine with our model forgetting about butterfly anatomy,” he says.  

Cosine claims that its generative coding assistant, called Genie, tops the leaderboard on SWE-Bench, a standard set of tests for coding models. Poolside is still building its model but claims that what it has so far already matches the performance of GitHub’s Copilot.

“I personally have a very strong belief that large language models will get us all the way to being as capable as a software developer,” says Kant.

Not everyone takes that view, however.

Illogical LLMs

To Justin Gottschlich, the CEO and founder of Merly, large language models are the wrong tool for the job—period. He invokes his dog: “No amount of training for my dog will ever get him to be able to code, it just won’t happen,” he says. “He can do all kinds of other things, but he’s just incapable of that deep level of cognition.”  

Having worked on code generation for more than a decade, Gottschlich has a similar sticking point with large language models. Programming requires the ability to work through logical puzzles with unwavering precision. No matter how well large language models may learn to mimic what human programmers do, at their core they are still essentially statistical slot machines, he says: “I can’t train an illogical system to become logical.”

Instead of training a large language model to generate code by feeding it lots of examples, Merly does not show its system human-written code at all. That’s because to really build a model that can generate code, Gottschlich argues, you need to work at the level of the underlying logic that code represents, not the code itself. Merly’s system is therefore trained on an intermediate representation—something like the machine-readable notation that most programming languages get translated into before they are run.

Gottschlich won’t say exactly what this looks like or how the process works. But he throws out an analogy: There’s this idea in mathematics that the only numbers that have to exist are prime numbers, because you can calculate all other numbers using just the primes. “Take that concept and apply it to code,” he says.

Not only does this approach get straight to the logic of programming; it’s also fast, because millions of lines of code are reduced to a few thousand lines of intermediate language before the system analyzes them.

Shifting mindsets

What you think of these rival approaches may depend on what you want generative coding assistants to be.  

In November, Cosine banned its engineers from using tools other than its own products. It is now seeing the impact of Genie on its own engineers, who often find themselves watching the tool as it comes up with code for them. “You now give the model the outcome you would like, and it goes ahead and worries about the implementation for you,” says Yang Li, another Cosine cofounder.

Pullen admits that it can be baffling, requiring a switch of mindset. “We have engineers doing multiple tasks at once, flitting between windows,” he says. “While Genie is running code in one, they might be prompting it to do something else in another.”

These tools also make it possible to protype multiple versions of a system at once. Say you’re developing software that needs a payment system built in. You can get a coding assistant to simultaneously try out several different options—Stripe, Mango, Checkout—instead of having to code them by hand one at a time.

Genie can be left to fix bugs around the clock. Most software teams use bug-reporting tools that let people upload descriptions of errors they have encountered. Genie can read these descriptions and come up with fixes. Then a human just needs to review them before updating the code base.

No single human understands the trillions of lines of code in today’s biggest software systems, says Li, “and as more and more software gets written by other software, the amount of code will only get bigger.”

This will make coding assistants that maintain that code for us essential. “The bottleneck will become how fast humans can review the machine-generated code,” says Li.

How do Cosine’s engineers feel about all this? According to Pullen, at least, just fine. “If I give you a hard problem, you’re still going to think about how you want to describe that problem to the model,” he says. “Instead of writing the code, you have to write it in natural language. But there’s still a lot of thinking that goes into that, so you’re not really taking the joy of engineering away. The itch is still scratched.”

Some may adapt faster than others. Cosine likes to invite potential hires to spend a few days coding with its team. A couple of months ago it asked one such candidate to build a widget that would let employees share cool bits of software they were working on to social media. 

The task wasn’t straightforward, requiring working knowledge of multiple sections of Cosine’s millions of lines of code. But the candidate got it done in a matter of hours. “This person who had never seen our code base turned up on Monday and by Tuesday afternoon he’d shipped something,” says Li. “We thought it would take him all week.” (They hired him.)

But there’s another angle too. Many companies will use this technology to cut down on the number of programmers they hire. Li thinks we will soon see tiers of software engineers. At one end there will be elite developers with million-dollar salaries who can diagnose problems when the AI goes wrong. At the other end, smaller teams of 10 to 20 people will do a job that once required hundreds of coders. “It will be like how ATMs transformed banking,” says Li.

“Anything you want to do will be determined by compute and not head count,” he says. “I think it’s generally accepted that the era of adding another few thousand engineers to your organization is over.”

Warp drives

Indeed, for Gottschlich, machines that can code better than humans are going to be essential. For him, that’s the only way we will build the vast, complex software systems that he thinks we will eventually need. Like many in Silicon Valley, he anticipates a future in which humans move to other planets. That’s only going to be possible if we get AI to build the software required, he says: “Merly’s real goal is to get us to Mars.”

Gottschlich prefers to talk about “machine programming” rather than “coding assistants,” because he thinks that term frames the problem the wrong way. “I don’t think that these systems should be assisting humans—I think humans should be assisting them,” he says. “They can move at the speed of AI. Why restrict their potential?”

“There’s this cartoon called The Flintstones where they have these cars, but they only move when the drivers use their feet,” says Gottschlich. “This is sort of how I feel most people are doing AI for software systems.”

“But what Merly’s building is, essentially, spaceships,” he adds. He’s not joking. “And I don’t think spaceships should be powered by humans on a bicycle. Spaceships should be powered by a warp engine.”

If that sounds wild—it is. But there’s a serious point to be made about what the people building this technology think the end goal really is.

Gottschlich is not an outlier with his galaxy-brained take. Despite their focus on products that developers will want to use today, most of these companies have their sights on a far bigger payoff. Visit Cosine’s website and the company introduces itself as a “Human Reasoning Lab.” It sees coding as just the first step toward a more general-purpose model that can mimic human problem-solving in a number of domains.

Poolside has similar goals: The company states upfront that it is building AGI. “Code is a way of formalizing reasoning,” says Kant.

Wang invokes agents. Imagine a system that can spin up its own software to do any task on the fly, he says. “If you get to a point where your agent can really solve any computational task that you want through the means of software—that is a display of AGI, essentially.”

Down here on Earth, such systems may remain a pipe dream. And yet software engineering is changing faster than many at the cutting edge expected. 

“We’re not at a point where everything’s just done by machines, but we’re definitely stepping away from the usual role of a software engineer,” says Cosine’s Pullen. “We’re seeing the sparks of that new workflow—what it means to be a software engineer going into the future.”

Meta’s new AI model can translate speech from more than 100 languages

Meta has released a new AI model that can translate speech from 101 different languages. It represents a step toward real-time, simultaneous interpretation, where words are translated as soon as they come out of someone’s mouth. 

Typically, translation models for speech use a multistep approach. First they translate speech into text. Then they translate that text into text in another language. Finally, that translated text is turned into speech in the new language. This method can be inefficient, and at each step, errors and mistranslations can creep in. But Meta’s new model, called SeamlessM4T, enables more direct translation from speech in one language to speech in another. The model is described in a paper published today in Nature. 

Seamless can translate text with 23% more accuracy than the top existing models. And although another model, Google’s AudioPaLM, can technically translate more languages—113 of them, versus 101 for Seamless—it can translate them only into English. SeamlessM4T can translate into 36 other languages.

The key is a process called parallel data mining, which finds instances when the sound in a video or audio matches a subtitle in another language from crawled web data. The model learned to associate those sounds in one language with the matching pieces of text in another. This opened up a whole new trove of examples of translations for their model.

“Meta has done a great job having a breadth of different things they support, like text-to-speech, speech-to-text, even automatic speech recognition,” says Chetan Jaiswal, a professor of computer science at Quinnipiac University, who was not involved in the research. “The mere number of languages they are supporting is a tremendous achievement.”

Human translators are still a vital part of the translation process, the researchers say in the paper, because they can grapple with diverse cultural contexts and make sure the same meaning is conveyed from one language into another. This step is important, says Lynne Bowker, Canada Research Chair in Translation, Technologies and Society at Université Laval in Quebec, who didn’t work on Seamless. “Languages are a reflection of cultures, and cultures have their own ways of knowing things,” she says. 

When it comes to applications like medicine or law, machine translations need to be thoroughly checked by a human, she says. If not, misunderstandings can result. For example, when Google Translate was used to translate public health information about the covid-19 vaccine from the Virginia Department of Health in January 2021, it translated “not mandatory” in English into “not necessary” in Spanish, changing the whole meaning of the message.

AI models have much more examples to train on in some languages than others. This means current speech-to-speech models may be able to translate a language like Greek into English, where there may be many examples, but cannot translate from Swahili to Greek. The team behind Seamless aimed to solve this problem by pre-training the model on millions of hours of spoken audio in different languages. This pre-training allowed it to recognize general patterns in language, making it easier to process less widely spoken languages because it already had some baseline for what spoken language is supposed to sound like.  

The system is open-source, which the researchers hope will encourage others to build upon its current capabilities. But some are skeptical of how useful it may be compared with available alternatives. “Google’s translation model is not as open-source as Seamless, but it’s way more responsive and fast, and it doesn’t cost anything as an academic,” says Jaiswal.

The most exciting thing about Meta’s system is that it points to the possibility of instant interpretation across languages in the not-too-distant future—like the Babel fish in Douglas Adams’ cult novel The Hitchhiker’s Guide to the Galaxy. SeamlessM4T is faster than existing models but still not instant. That said, Meta claims to have a newer version of Seamless that’s as fast as human interpreters. 

“While having this kind of delayed translation is okay and useful, I think simultaneous translation will be even more useful,” says Kenny Zhu, director of the Arlington Computational Linguistics Lab at the University of Texas at Arlington, who is not affiliated with the new research.

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.

ANTHROPIC

“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.”

How optimistic are you about AI’s future?

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

The start of a new year, and maybe especially this one, feels like a good time for a gut check: How optimistic are you feeling about the future of technology? 

Our annual list of 10 Breakthrough Technologies, published on Friday, might help you decide. It’s the 24th time we’ve published such a list. But just like our earliest picks (2001’s list featured brain-computer interfaces and ways to track copyrighted content on the internet, by the way), this year’s technologies may come to help society, harm it, or both.

Artificial intelligence powers four of the breakthroughs featured on the list, and I expect your optimism about them will vary widely. Take generative AI search. Now becoming the norm on Google with its AI Overviews, it promises to help sort through the internet’s incomprehensible volume of information to offer better answers for the questions we ask. Along the way, it is upending the model of how content creators get paid, and positioning fallible AI as the arbiter of truth and facts. Read more here. 

Also making the list is the immense progress in the world of robots, which can now learn faster thanks to AI. This means we will soon have to wrestle with whether we will trust humanoid robots enough to welcome them into our most private spaces, and how we will feel if they are remotely controlled by human beings working abroad. 

The list also features lots of technologies outside the world of AI, which I implore you to read about if only for a reminder of just how much other scientific progress is being made. This year may see advances in studying dark matter with the largest digital camera ever made for astronomy, reducing emissions from cow burps, and preventing HIV with an injection just once every six months. We also detail how technologies that you’ve long heard about—from robotaxis to stem cells—are finally making good on some of their promises.

This year, the cultural gulf between techno-optimists and, well, everyone else is set to widen. The incoming administration will be perhaps the one most shaped by Silicon Valley in recent memory, thanks to Donald Trump’s support from venture capitalists like Marc Andreessen (the author of the Techno-Optimist Manifesto) and his relationship, however recently fraught, with Elon Musk. Those figures have critiqued the Biden administration’s approach to technology as slow, “woke,” and overly cautious—attitudes they have vowed to reverse. 

So as we begin a year of immense change, here’s a small experiment I’d encourage you to do. Think about your level of optimism for technology and what’s driving it. Read our list of breakthroughs. Then see how you’ve shifted. I suspect that, like many people, you’ll find you don’t fit neatly in the camp of either optimists or pessimists. Perhaps that’s where the best progress will be made. 

Now read the rest of The Algorithm

Deeper Learning

The biggest AI flops of 2024

Though AI has remained in the spotlight this year (and even contributed to Nobel Prize–winning research in chemistry), it has not been without its failures. Take a look back over the year’s top AI failures, from chatbots dishing out illegal advice to dodgy AI-generated search results. 

Why it matters: These failures show that there are tons of unanswered questions about the technology, including who will moderate what it produces and how, whether we’re getting too trusting of the answers that chatbots produce, and what we’ll do with the mountain of “AI slop” that is increasingly taking over the internet. Above all, they illustrate the many pitfalls of blindly shoving AI into every product we interact with.

Bits and Bytes

What it’s like being a pedestrian in the world of Waymos 

Tech columnist Geoffrey Fowler finds that Waymo robotaxis regularly fail to stop for him at a crosswalk he uses every day. Though you can sometimes make eye contact with human drivers to gauge whether they’ll stop, Waymos lack that “social intelligence,” Fowler writes. (The Washington Post)

The AI Hype Index

For each print issue, MIT Technology Review publishes an AI Hype Index, a highly subjective take on the latest buzz about AI. See where facial recognition, AI replicas of your personality, and more fall on the index. (MIT Technology Review)

What’s going on at the intersection of AI and spirituality

Modern religious leaders are experimenting with A. just as earlier generations examined radio, television, and the internet. They include Rabbi Josh Fixler, who created “Rabbi Bot,” a chatbot trained on his old sermons. (The New York Times)

Meta has appointed its most prominent Republican to lead its global policy team

Just two weeks ahead of Donald Trump’s inauguration, Meta has announced it will appoint Joel Kaplan, who was White House deputy chief of staff under George W. Bush, to the company’s top policy role. Kaplan will replace Nick Clegg, who has led changes on content and elections policies. (Semafor)

Apple has settled a privacy lawsuit against Siri

The company has agreed to pay $95 million to settle a class action lawsuit alleging that Siri could be activated accidentally and then record private conversations without consent. The news comes after MIT Technology Review reported that Apple was looking into whether it could get rid of the need to use a trigger phrase like “Hey Siri” entirely. (The Washington Post)