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.”