The hidden scaling cliff that’s about to break your agent rollouts

On Wednesday, President Trump issued three executive orders, delivered a speech, and released an action plan, all on the topic of continuing American leadership in AI.  The plan contains dozens of proposed actions, grouped into three “pillars”: accelerating innovation, building infrastructure, and leading international diplomacy and security. Some of its recommendations are thoughtful even if incremental, some clearly serve ideological ends, and many enrich big tech companies, but the plan is just a set of recommended actions.  The three executive orders, on the other hand, actually operationalize one subset of actions from each pillar:  One aims to prevent “woke AI” by mandating that the federal government procure only large language models deemed “truth-seeking” and “ideologically neutral” rather than ones allegedly favoring DEI. This action purportedly accelerates AI innovation. A second aims to accelerate construction of AI data centers. A much more industry-friendly version of an order issued under President Biden, it makes available rather extreme policy levers, like effectively waiving a broad swath of environmental protections, providing government grants to the wealthiest companies in the world, and even offering federal land for private data centers. A third promotes and finances the export of US AI technologies and infrastructure, aiming to secure American diplomatic leadership and reduce international dependence on AI systems from adversarial countries. This flurry of actions made for glitzy press moments, including an hour-long speech from the president and onstage signings. But while the tech industry cheered these announcements (which will swell their coffers), they obscured the fact that the administration is currently decimating the very policies that enabled America to become the world leader in AI in the first place.
To maintain America’s leadership in AI, you have to understand what produced it. Here are four specific long-standing public policies that helped the US achieve this leadership—advantages that the administration is undermining.  Investing federal funding in R&D  Generative AI products released recently by American companies, like ChatGPT, were developed with industry-funded research and development. But the R&D that enables today’s AI was actually funded in large part by federal government agencies—like the Defense Department, the National Science Foundation, NASA, and the National Institutes of Health—starting in the 1950s. This includes the first successful AI program in 1956, the first chatbot in 1961, and the first expert systems for doctors in the 1970s, along with breakthroughs in machine learning, neural networks, backpropagation, computer vision, and natural-language processing.
American tax dollars also funded advances in hardware, communications networks, and other technologies underlying AI systems. Public research funding undergirded the development of lithium-ion batteries, micro hard drives, LCD screens, GPS, radio-frequency signal compression, and more in today’s smartphones, along with the chips used in AI data centers, and even the internet itself. Instead of building on this world-class research history, the Trump administration is slashing R&D funding, firing federal scientists, and squeezing leading research universities. This week’s action plan recommends investing in R&D, but the administration’s actual budget proposes cutting nondefense R&D by 36%. It also proposed actions to better coordinate and guide federal R&D, but coordination won’t yield more funding. Some say that companies’ R&D investments will make up the difference. However, companies conduct research that benefits their bottom line, not necessarily the national interest. Public investment allows broad scientific inquiry, including basic research that lacks immediate commercial applications but sometimes ends up opening massive markets years or decades later. That’s what happened with today’s AI industry. Supporting immigration and immigrants Beyond public R&D investment, America has long attracted the world’s best researchers and innovators. Today’s generative AI is based on the transformer model (the T in ChatGPT), first described by a team at Google in 2017. Six of the eight researchers on that team were born outside the US, and the other two are children of immigrants.  This isn’t an exception. Immigrants have been central to American leadership in AI. Of the 42 American companies included in the 2025 Forbes ranking of the 50 top AI startups, 60% have at least one immigrant cofounder, according to an analysis by the Institute for Progress. Immigrants also cofounded or head the companies at the center of the AI ecosystem: OpenAI, Anthropic, Google, Microsoft, Nvidia, Intel, and AMD. “Brain drain” is a term that was first coined to describe scientists’ leaving other countries for the US after World War II—to the Americans’ benefit. Sadly, the trend has begun reversing this year. Recent studies suggest that the US is already losing its AI talent edge through the administration’s anti-immigration actions (including actions taken against AI researchers) and cuts to R&D funding. Banning noncompetes Attracting talented minds is only half the equation; giving them freedom to innovate is just as crucial.

Silicon Valley got its name because of mid-20th-century companies that made semiconductors from silicon, starting with the founding of Shockley Semiconductor in 1955. Two years later, a group of employees, the “Traitorous Eight,” quit to launch a competitor, Fairchild Semiconductor. By the end of the 1960s, successive groups of former Fairchild employees had left to start Intel, AMD, and others collectively dubbed the “Fairchildren.”  Software and internet companies eventually followed, again founded by people who had worked for their predecessors. In the 1990s, former Yahoo employees founded WhatsApp, Slack, and Cloudera; the “PayPal Mafia” created LinkedIn, YouTube, and fintech firms like Affirm. Former Google employees have launched more than 1,200 companies, including Instagram and Foursquare. AI is no different. OpenAI has founders that worked at other tech companies and alumni who have gone on to launch over a dozen AI startups, including notable ones like Anthropic and Perplexity. This labor fluidity and the innovation it has created were possible in large part, according to many historians, because California’s 1872 constitution has been interpreted to prohibit noncompete agreements in employment contracts—a statewide protection the state originally shared only with North Dakota and Oklahoma. These agreements bind one in five American workers. Last year, the Federal Trade Commission under President Biden moved to ban noncompetes nationwide, but a Trump-appointed federal judge has halted the action. The current FTC has signaled limited support for the ban and may be comfortable dropping it. If noncompetes persist, American AI innovation, especially outside California, will be limited. Pursuing antitrust actions One of this week’s announcements requires the review of FTC investigations and settlements that “burden AI innovation.” During the last administration the agency was reportedly investigating Microsoft’s AI actions, and several big tech companies have settlements that their lawyers surely see as burdensome, meaning this one action could thwart recent progress in antitrust policy. That’s an issue because, in addition to the labor fluidity achieved by banning noncompetes, antitrust policy has also acted as a key lubricant to the gears of Silicon Valley innovation.  Major antitrust cases in the second half of the 1900s, against AT&T, IBM, and Microsoft, allowed innovation and a flourishing market for semiconductors, software, and internet companies, as the antitrust scholar Giovanna Massarotto has described. William Shockley was able to start the first semiconductor company in Silicon Valley only because AT&T had been forced to license its patent on the transistor as part of a consent decree resolving a DOJ antitrust lawsuit against the company in the 1950s. 
The early software market then took off because in the late 1960s, IBM unbundled its software and hardware offerings as a response to antitrust pressure from the federal government. As Massarotto explains, the 1950s AT&T consent decree also aided the flourishing of open-source software, which plays a major role in today’s technology ecosystem, including the operating systems for mobile phones and cloud computing servers. Meanwhile, many attribute the success of early 2000s internet companies like Google to the competitive breathing room created by the federal government’s antitrust lawsuit against Microsoft in the 1990s. 
Over and over, antitrust actions targeting the dominant actors of one era enabled the formation of the next. And today, big tech is stifling the AI market. While antitrust advocates were rightly optimistic about this administration’s posture given key appointments early on, this week’s announcements should dampen that excitement.  I don’t want to lose focus on where things are: We should want a future in which lives are improved by the positive uses of AI.  But if America wants to continue leading the world in this technology, we must invest in what made us leaders in the first place: bold public research, open doors for global talent, and fair competition.  Prioritizing short-term industry profits over these bedrock principles won’t just put our technological future at risk—it will jeopardize America’s role as the world’s innovation superpower.  Asad Ramzanali is the director of artificial intelligence and technology policy at the Vanderbilt Policy Accelerator. He previously served as the chief of staff and deputy director of strategy of the White House Office of Science and Technology Policy under President Biden.

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American streets are incredibly dangerous for pedestrians. A San Carlos, California-based startup called Obvio thinks it can change that by installing cameras at stop signs — a solution the founders also say won’t create a panopticon. 

That’s a bold claim at a time when other companies like Flock have been criticized for how its license plate-reading cameras have become a crucial tool in an overreaching surveillance state. 

Obvio founders Ali Rehan and Dhruv Maheshwari believe they can build a big enough business without indulging those worst impulses. They’ve designed the product with surveillance and data-sharing limitations to ensure they can follow through with that claim.

They’ve found deep pockets willing to believe them, too. The company has just completed a $22 million Series A funding round led by Bain Capital Ventures. Obvio plans to use those funds to expand beyond the first five cities where it’s currently operating in Maryland. 

Rehan and Maheshwari met while working at Motive, a company that makes dashboard cameras for the trucking industry. While there, Maheshwari told TechCrunch the pair realized “a lot of other normal passenger vehicles are awful drivers.” 

The founders said they were stunned the more they looked into road safety. Not only were streets and crosswalks getting more dangerous for pedestrians, but in their eyes, the U.S. was also falling behind on enforcement. 

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“Most other countries are actually pretty good at this,” Maheshwari said. “They have speed camera technology. They have a good culture of driving safety. The U.S. is actually one of the worst across all the modern nations.”

Maheshwari and Rehan began studying up on road safety by reading books and attending conferences. They found that people in the industry gravitated toward three general solutions: education, engineering, and enforcement. 

In their eyes, those approaches were often too separated from each other. It’s hard to quantify the impact of educational efforts. Local officials may try to fix a problematic intersection by, say, installing a roundabout, but that can take years of work and millions of dollars. And law enforcement can’t camp out at every stop sign.

Rehan and Maheshwari saw promise in combining them. 

The result is a pylon (often brightly-colored) topped with a solar-powered camera that can be installed near almost any intersection. It’s designed not to blend in — part of the education and awareness aspect — and it’s also carefully engineered to be cheap and easy to install.

The on-device AI is trained to spot the worst types of stop sign or other infractions. (The company also claims on its website it can catch speeding, crosswalk violations, illegal turns, unsafe lane changes, and even distracted driving.) When one of these things happen, the system matches a car’s license plate to the state’s DMV database. 

All of that information — the accuracy of the violation, the license plate — is verified by either Obvio staff or contractors before it’s sent to law enforcement, which then has to review the infractions before issuing a citation.

Obvio gives the tech to municipalities for free and makes money from the citations. Exactly how that citation revenue will get split between Obvio and the governments will vary from place to place, as Maheshwari said regulations about such agreements differ by state.

That clearly creates an incentive for increasing the number of citations. But Rehan and Maheshwari said they can build a business around stopping the worst offenses across a wide swath of American cities. They also said they want Obvio to remain present in — and responsive to — the communities that use their tech.

“Automated enforcement should be used in conjunction with community advocacy and community support, it shouldn’t be this camera that you put up that does revenue grab[s] and gotchas,” Maheshwari said. The goal is to “start using these cameras in a way to warn and deter the most egregious drivers [so] you can actually create communitywide support and behavior change.”

Cities and their citizens “need to trust us,” Maheshwari said. 

There’s also a technological explanation for why Obvio’s cameras may not become an overpowered surveillance tool for law enforcement beyond their intended use.

Obvio’s camera pylon records and processes its footage locally. It’s only when a violation is spotted that the footage leaves the device. Otherwise, all other footage of vehicles and pedestrians passing through a given intersection stays on the device for about 12 hours before it gets deleted. (The footage is also technically owned by the municipalities, which have remote access.)

This doesn’t eliminate the chance that law enforcement will use the footage to surveil citizens in other ways. But it does reduce that chance.

That focus is what drove Bain Capital Ventures partner Ajay Agarwal to invest in Obvio.

“Yes, in the short term, you can maximize profits, and erode those values, but I think over time, it will limit the ability of this company to be ubiquitous. It’ll create enemies or create people who don’t want this,” he told TechCrunch. “Great founders are willing to sacrifice entire lines of business, frankly, and lots of revenue, in pursuit of the ultimate mission.”

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