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In the foreground, multiple people are surrounded by computer screens filled with data visualizations, charts, and technical information. A woman seated in an armchair appears deep in thought, surrounded by data-filled monitors. Beside her, a man leans over, using a tablet to assist with their inspection of a plant or tree. In the centre, a figure holds a large frame or screen displaying anatomical illustrations, representing the use of AI to analyse medical imagery. To the left, another person is intently observing a computer screen, while a second figure nearby is deeply immersed in analysing data. A woman dominates the right side of the composition, gazing upwards as if in contemplation or envisioning something beyond the immediate scene. The background features more people, including a family holding hands, and other abstract representations of data.

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In Brazil, the debate over AI development is obsessed with frontier models and data centers, but that obsession has a blind spot. The vast gamut of AI issues goes way beyond that, and for a country that aims to reindustrialize, edge AI, which is when the inference that runs on local hardware rather than in distant data centers, deserves a much louder debate.

The country's biggest AI move so far runs the other way, toward data centralization, supercomputers, and new hyperscale data centers. In August, the Brazilian government 

 it will invest 2.3 billion reais ($444.2 million) in two AI projects, including a supercomputing partnership with China’s Huawei and iFlytek and a tender for a second supercomputers. These supercomputers will run applications built on intensive, centralized data collected and entrusted to the government, powering AI public services such as digital public tools, automated government procurement assistance, and telehealth data transcription. According to the Brazilian Minister of Management and Innovation in Public Services, Esther Dweck, this is a 

strategy dubbed “Base de Dados Brasil” which

 aims to centralize, integrate, and safeguard Brazil's citizens data to be used in AI training. But Brazil is a country of contrasts: it ranks fourth for wealth inequality among the 56 markets in UBS’s 

. Its public investments also contrast, reflecting how resources are distributed.

For example, computer labs are present in only 27% of public schools serving elementary grades, rising to only 73% of public high schools, according to 2024 

 compiled by Todos Pela Educação. A Regional Center for Studies on the Development of the Information Society (Cetic.br) 2025 

 also found that video surveillance cameras are present in 79% of schools in the country's state capitals.

And so, while there's a lively debate over access to Nvidia's latest chips and where to build new data centers, and officials talk about bringing hyperscale infrastructure into the country to pool the data Brazilian citizens generate through public infrastructure, 84% of employed young Brazilians work in jobs that don't require specific qualifications, and only 1 in 7 land a technical or higher-level post, according 

to Brazil's Ministry of Labor and Employment

. Only 12% of high school students want to pursue engineering, and math is a major reason why, 

 by CIEE and Instituto Locomotiva. Young Brazilians rate their confidence with the subject as just 5.2 out of 10, and 79% say gaps in basic education are a barrier to continue their studies. In the Program for International Student Assessment (PISA) 

 published in September, Brazil again scored below the Organization for Economic Cooperation and Development (OECD) average in math.

So, debating only frontier models and hyperscalers is largely buying the vendor's argument dressed up as a development argument. Many public and private sector AI applications in Brazil don't need frontier capabilities. They need reliable, bounded, locally controlled inference — 

 as “edge AI” — and workers ready to fix and maintain this infrastructure.

Why edge AI matters for countries in need of industrial development

Edge AI is a fancy name for a type of data processing realized locally, through small, specialized, well-scoped models that beat chasing frontier scale in industrial data processing, manufacturing, as well as agricultural and lower-tier industrial automation, like defect detection on textile or footwear lines, predictive maintenance in packaging and food processing, or structural monitoring on mining sites in general.

This kind of automation offers a diversified path — provided countries build the standardized, shared policy and funding framework that lets a new economy grow around it, the way China has sought to. China's industrial AI strategy, as mapped in a 

2025 World Economic Forum and Accenture review

, is organized by what each sector actually needs to run. Intelligent transport requires low-latency computing for real-time monitoring and data analysis, while smart manufacturing demands highly precise, specialized computing capabilities. Chinese firms, the report notes, “often find it beneficial to prioritize specialized, smaller models tailored to specific scenarios” than to chase frontier capabilities. The state backs this with layered policy: Beijing sets strategic direction, provinces and cities build AI clusters around their own industrial strengths, and a national program (East Data, West Computing) physically relocates computing capacity toward western regions with abundant renewable energy. This is infrastructure matched to the job.

Brazil doesn't have to invent this model from scratch, but if the country is thinking about reindustrializing by adding a layer of high productivity with AI as the motor, it needs purpose-built edge inference technology, sufficient for the applications that matter most on the ground.

Local edge infrastructure is, at bottom, an industrial policy choice. Training people to maintain, adapt, and build on this hardware creates skilled technical employment that can't be offshored. It also creates the conditions for more local data storage and genuinely new, local approaches to processing that data. Compare that to the current trajectory: hyperscalers 

 generate plenty of jobs on paper, but a 

July 2026 Getulio Vargas Foundation (FGV) study

 estimated that, for a model 100-megawatt data center, 85% of the jobs are tied to the construction phase, and only about 4% become permanent operation and maintenance roles. The study suggests only 15% of jobs overall are permanent, counting spillover into other sectors. The risk is that what’s left is a thin layer of engineers working for data monopolies, and a large base of gig workers managed by algorithms they don't control. More investment in edge AI is a possible third path, but it needs intentional, focused policies that create islands of innovation inside the country.

, the federal industrial policy launched in January 2024, which sets aside 300 billion reais (about $58 billion) in financing through 2026, since expanded, for six missions with targets for 2033. NIB is one of the right vehicles for this argument, but in my view, it has yet to make it, lacking both regional integration and a grounding in local AI policy.

This isn't a fringe position, either: Andrew Ng, the co-founder of Google Brain, former chief scientist at Baidu, and now an advocate for what he calls "data-centric AI," has spent years 

 from inside the industry, that most manufacturing and industrial problems don't need a giant model, they need a small, specialized one fine-tuned on good data, sometimes as few as 50 examples, running close to the factory floor, as Ng pointed out in a 2022 interview with IEEE Spectrum.

Probably no one in the "west" wants to hear that there is something to learn from China, but Brazil is hedging its bets between the US and China, and so maybe it makes sense for the country to be a laboratory for how to adopt some of these approaches, and to distinguish itself from what cloud companies want to sell.

 states that the focus is to promote the digital transformation on the factory floor, 

 from Management and Innovation Minister Esther Dweck have framed the issue around centralized national data infrastructure with a unified digital identity, a national database meant to sustain AI development and enable more personalized public services, not edge computing, and not a plan for where the hardware that would actually deliver those service runs. It appears that the government's instinct keeps circling back to big, centralized datasets: identity, health, social benefits. That's a reasonable place to start, but it needs to run parallel to one that makes services more local, with purpose-built hardware that is less cloud and energy-demanding.

Brazil's own tax incentives point the same centralized way. The 

, (Lei 15.504/2026, signed September 2026) suspends federal taxes on electronic components and IT equipment, but only for companies with approved data center projects, a status that requires meeting sustainability requirements and commitments such as offering part of their capacity to the domestic market or investing in innovation. That explicitly excludes any company enrolled in Simples Nacional, Brazil's tax regime for small and micro businesses. The country's major new AI-adjacent tax break is built for hyperscale infrastructure, not for the small electronics shops, repair technicians, or edge-hardware integrators I argue deserve the investment instead.

Of course, edge hardware still requires supply chain continuity, firmware support, and maintenance capacity. That doesn't disappear. But it becomes an opportunity rather than a dependency, because building that capacity locally is the point. Brazil already has a large, mostly informal repair workforce that keeps old phones and electronics running, most of it operating outside formal registration, in a sector where informality likely runs above Brazil's national rate of 37.5%, according to Brazilian Institute of Geography and Statistics (IBGE) 

continuous national household sample survey data

480 million computers, tablets and smartphones

 in active use in the country, the repair market sustains a large amount of highly specialized technical labor. Not exactly the same skills edge hardware will require, but proof of the country's underlying capacity. That market is also precarious: informal, often unlicensed, and its workers 

are sometimes treated as targets by police rather than as technicians

. Without them, most Brazilians wouldn't be able to keep their phones running or afford new ones. Edge AI policy could formalize and extend that exact repair logic to a new class of hardware, and it doesn't have to happen alone. Brazil could partner with foreign firms to train workers domestically, the same way it partners for everything else it doesn't yet make at scale.

Brazil already has centrally standardized Digital Public Infrastructure (DPI) that allows dozens of private and public companies to plug into shared protocols, such as the Pix payment system. However, another big potential for the country resides at the margins, in the opportunity to automate what already exists or create new manufacturing lines supported by edge AI. One example: the CESAR innovation center’s Manaus unit and UCB Power 

 for inspecting battery components at UCB Power's factory in the Amazon, using a 4K camera and edge AI to process visual data locally, without the need to send images back and forth to the cloud.

The scarce resource in edge AI is arguably not silicon but technicians. While the market-spread model of centralized, frontier-scale AI runs in resource consuming data centers, uses data that continuously creates risk, can draw on an underlying surveillance architecture, and offers a small layer of superjobs for elite engineers, the edge layer offers a much more favorable industrial policy that goes beyond hardware and software, leveraging a policy that upskills and converts the existing workforce, like technicians registered micro-Entrepreneurs, underemployed STEM grads, gig workers, and others, into registered, higher-value technical laborers. This would be a jobs-first industrial policy that requires hardware, software, and training — not the opposite.

Anthropic's IPO marketing kicks off this week. As is typical for investor disclosures in the age of AI, its 

 is full of warnings to investors, from ballooning computing costs to the possible extinction of humanity. Despite such a broad sweep, the S-1 may be missing a particular risk: that the United States government might try to seize control of the company, and that it might be able to do so at a steep discount to Anthropic's 

The federal government (and its state and local counterparts) has broad power to seize private property: anything from land to copyrights and patents to corporate stock. The Fifth Amendment's 

 requires "just compensation" for the seizure of private property, but doesn't otherwise limit *what* the government can seize.

How might this work? Imagine a company with a single class of common stock: 100 million shares outstanding at $2/share. A very simple model of a seizure is that the government would seize all of the company's common stock and pay shareholders $2/share. The government would then control the company, at a cost of $200 million.

So far, so simple. But things might get complicated, even in our simple example. The company's shareholders might try to go to court to dispute the price — they might argue that an acquisition would command a premium, for example. If the president attempted to unilaterally seize the company, the shareholders might argue that the president needed to get 

 from Congress before doing so; that the emergency wasn't extreme enough to warrant executive action. But if a court decides the government appropriately authorized the seizure, it would probably take effect, with a compensation of more or less $200 million.

But public debt is spiraling, and maybe the government wants to pinch pennies. Instead of spending $200 million to buy all the stock, it could spend $102 million to seize 51% of the stock. As the majority shareholder, the government would effectively control the company: it could hire and fire board members and executives. There still might be lawsuits: here, the minority shareholders might argue that this was an "effective taking", especially if it destroys the value of their shares. That's not a sure thing: it's an easier case if the government shuts the company down, but less so if the government continues to run the business more or less as is.

Here's where it gets weird. In this toy example, the company had a single class of stock: control a majority, and you control the company. A shareholder's voting power in the company was the same as their economic interest in it. But many tech companies aren't organized this way. Instead, they have multiple classes of stock that give founders increased voting power. For example: Mark Zuckerberg 

 about 13% of Meta stock, but controls 61% of its voting power. At 

, while each class of stock has equivalent economic rights, its Class B stock for founders and other company leaders grants 10 votes per share, while its Class A stock grants 1 vote. Alphabet's Class C stock carries no voting rights, and trades at a slight discount. The newly for-profit 

 is controlled by a nonprofit that holds 26% of its stock but has the power to appoint all of the company’s board members. For tech founders who need to raise capital, this dual-class structure has been an appealing solution to attract outside investment without giving up control over the company's direction.

Anthropic takes this structure to an extreme. A long-term benefit trust will control a "Class T" share, which permits it to appoint a majority of the company's board of directors. And a "Founder LLC" will own a "Class F" share, which has 50.1% of voting power. Anthropic's S-1 states that neither class of share will have any claim to the economic value of the company—all or nearly all of the company's economic value will lie with other shareholders. But: controlling both the Class T and the Class F share will effectively control the company. As Anthropic says in 

: "Under this governance structure, we expect that the LTBT and the Founder LLC will collectively have the ability to elect all members of our board of directors, and the Founder LLC will have the ability to determine the outcome of all matters submitted to a vote of our stockholders generally for the foreseeable future."

So what would happen if the government tried to seize the company? Not the whole of Anthropic, but just the shares that control how the company is run. The reason is almost immaterial: say the company was 90% of the way to an especially dangerous or difficult-to-control technology; or produced a bioweapon in its wet lab; or a new hot war ripped away whatever restraint the government may have had. Or maybe the company gets on the government's bad side again, or the government decides it wants to control an AI lab.

What would the government be required to pay, in order to seize two shares with zero economic value? Here, the "just compensation" would likely not be zero—controlling Anthropic is worth something, after all. But it might be well south of $1.4 trillion; downright affordable, even. Economic shareholders may have a surprisingly weak case for compensation: Anthropic’s Public Benefit Corporation status allows directors and officers to take actions that may advance the company’s purpose even when it does not maximize benefits to shareholders. Anthropic’s S-1 explicitly warns that the company’s Trust may exercise its powers in a way that is adverse to other shareholders’ economic interests. Still, complications are likely: shareholders would sue; engineers could flee; the market would respond. Seizing two controlling shares would be a fairly novel legal approach that a court might view with skepticism, to say nothing of where it might land on the subject of just compensation. But in the right circumstances—or the right emergency—the government might take the risk.

This vulnerability is not unique to Anthropic. Other AI companies with imbalanced governance structures may be seizable for less than their full economic value. This is the cost of concentrating control in a small number of people, while working on a capable, potentially dangerous technology.

This may be unlikely to happen anytime soon. But if it does happen, the AI industry may have only itself to blame. After all, AI companies have spent enormous energy warning the public about the danger of their work, and how they cannot be trusted to wield this power alone. What if one day the government finally believed them?

At first glance, it sounds like a normal modeling job. But on closer inspection, the details become peculiar. The job is in Pakistan, the shift lasts from 9 p.m. to 9 a.m., and the pay is $4,000-$5,000 a month, or nearly three times Pakistan’s per capita income.

The recruiter, who has a Chinese username, is also looking for a “real model” of any nationality in Vietnam to target “European love stories,” and another model from South Asia to work in Malaysia.

The ad was posted on Telegram, in a channel advertising jobs in Cambodia.

These are the characteristics of ads recruiting workers, usually attractive women, to participate in video calls for romance scams, according to the analysis by Moses Ngeth, a Cambodian security analyst who has reviewed thousands of scam job ads on Telegram channels over the past three years.

The ad in question was posted in late October, two months after the 

: "there are currently no online scam compounds remaining in the country.”

 on scam compounds in the country, after years of international pressure and reports exposing the billion-dollar illicit industry that runs on trafficked workers forced to con victims around the world. But beyond the images of emptied buildings, and confiscated phones, computers and Starlink terminals, is an industry that continues to evolve, thrive, and regroup in countries such as Myanmar, Vietnam, Timor-Leste, Pakistan and as far afield as Georgia and Nigeria.

“The scams follow you home,” Ngeth told Tech Policy Press, meaning scam operations often relocate to the home countries of their workers.

Recruitment in Telegram channels for scam jobs in Cambodia continues, and the roles increasingly appear to be filled less by trafficking victims and more by people who apply voluntarily, whether out of desperation or because they are attracted by the pay. Since the crackdown on Chinese-language recruitment Telegram channels, analysts at the UK-based Humanity Research Consultancy (HRC) have seen more individuals posting CVs listing their experience managing scam teams, the languages they speak, the markets they’ve targeted, and even the size of the teams they’ve supervised.

“To me, this is a sign that the scam economy is increasingly resembling a mature illicit labor market,” Mina Chiang, founder and director of HRC, told Tech Policy Press. “Rather than disappearing after a crackdown, experienced managers and workers are actively looking for their next employer, while syndicates continue recruiting for new locations. The knowledge, skills, and management systems move with the people, making the industry highly resilient.”

In fact, the crackdown has produced a humanitarian crisis, as thousands of scam workers became 

 and undocumented overnight, without money and legal support to get home safely.

“The government’s ‘crackdown’ has been done in a highly selective manner – closing physical facilities and arresting the slave laborers who have been forced to engage in online scamming, while avoiding any action against the vast majority of the bosses who own and manage the scam centers,” Mark Taylor, an independent consultant who previously worked for USAID in Cambodia, told Tech Policy Press.

The owners and managers of scam centers “are seeking to recruit stranded workers who already have the skills to conduct scams such as the time-intensive 

. Facing miserable and precarious conditions, some stranded scam workers consider accepting work again in the scam centers.”

Police reports, experts and scam job ads on Telegram indicate the bosses, most of them Chinese and Taiwanese, quickly moved to hotels, restaurants, apartments in Cambodia and other countries, including Myanmar, Vietnam, Timor-Leste, and as far as Croatia and Nigeria.

 said some returnees from scam compounds in Southeast Asia were later recruited by Chinese scam bosses to work in Madagascar after struggling with debt. They were offered substantial joining bonuses known as “red envelopes.”

 reported that since the start of the year, they had arrested more than 1,000 foreigners, most of them from China, Vietnam and India, for alleged involvement in cybercrime. Authorities and researchers said the increase stemmed from Chinese syndicates relocating from Cambodia and Myanmar, drawn by Sri Lanka’s relaxed visa rules, developed telecommunications infrastructure, and relatively weak cybercrime enforcement.

In October, police in north central Thanh Hoa province in Vietnam 

 and one Vietnamese with using digital means to misappropriate property and facilitating illegal stay in Vietnam. The indictment said the group rented a hotel beginning in June 2026 to conduct romance scams targeting victims in Japan, China, Taiwan, the US, India and South Korea. Their equipment and data had been transported from Cambodia.

Most of the accused are Chinese nationals, besides 28 Indians, nine Cambodians, one Malaysian and one Myanmar national. The majority entered Vietnam on tourist visas, while 22 entered illegally, 21 were without passports and one was on a wanted list. To evade detection and arrest, the group avoided hiring Vietnamese staff and targeting Vietnamese victims. The police estimate the scammers have 

Scammers who moved to Vietnam are mostly small operations. “They brought what they had learned at the scam centers (in Cambodia) to open one of their own,” Ngo Minh Hieu, prominent Vietnamese anti-scam investigator, told Tech Policy Press. “Cybercrime has become an ecosystem, with every step available as a service.”

Previously, opening a scam operation required operators to be well-versed in money laundering, know how to hack databases to steal large numbers of account credentials, and build a team from the ground up.

Now all it takes is a visit to Telegram, where Chinese businesses offer money laundering services, databases, and scam scripts, while phones and computers can be purchased second-hand. Hieu estimates that starting a 15-person operation in Vietnam would cost less than $4,000.

And if one wants to work in the scam industry, there are diverse jobs, from those requiring zero experience to more technical ones.

Ngeth, the Cambodian analyst, combed through 4,991 ads posted in Telegram channels between 2023 and 2026. According to his report, 

 the most common job advertised is “add contact,” which basically means finding people online to talk to, with the intention of eventually scamming them. It reportedly pays up to $750 a month, which is more than triple Cambodia’s minimum wage.

The model job mentioned at the beginning of this article is by far the most lucrative. These jobs typically require good English skills, and some ads specify a preference for Western-looking faces for video calls. Other jobs include “customer service,” “killer,” which means the person tasked with taking the victim’s money, translator, and HR and finance roles.

Then there are ads looking for money mules in Turkey, and an AI model developer based in Vietnam.

This indicates that while Cambodia has been home to the largest scam operations, “this is not only a Cambodian policy issue,” Chiang said.

The lack of protection for survivors of trafficking into scam operations has contributed to a humanitarian crisis, pushing some to return to scamming simply to earn enough money to survive. Meanwhile, the spread of smaller scam operations around the world makes them harder for authorities to detect and dismantle.

“The buildings can be shut down, but the people, the networks, and the experience remain,” Chiang said. “If those are not disrupted, the same business model can quickly reappear in a different form and in a different location.”

The massive Democratic sweep of congressional and state races across the United States was a referendum on many things: the increased cost of living, President Donald Trump’s unpopularity and corruption in his administration, the rollback of reproductive and LGBTQ+ rights, the damage caused by tariffs, and the war on Iran. But curbing the power of the tech oligarchy was high on voters’ minds.

 against Big Tech. Texas governor-elect Gina Hinojosa and South Carolina Senator-elect Annie Andrews, who won in what were among the night’s biggest upsets, 

. Michigan senator-elect Abdul El Sayed campaigned on a public “AI dividend” and 

 to regulate frontier models. Manny Rutinel, who co-sponsored the first comprehensive state-level AI regulation law back in 2024, 

 a House seat in Colorado with AI accountability as part of his platform. And around the country, ballot initiatives to curb data centers 

: they’re a visible place where people can focus their efforts to curb the power of big social media platforms and AI companies. But regulating data centers, or even AI, is by no means the 

 form of technology regulation that we need. Given this overwhelming voter mandate to enact tech regulation, I asked some of the top scholars and policy folks in this space one question: what’s on your wishlist to regulate technology, at either the state or the federal level?

The answers were big and small, more and less realistic, but all concrete. And while Trump has promised to veto Democratic legislation, many of these ideas are viable at the state level given new slates of enthusiastic Democratic leaders around the country.

Affordability is at the top of the populist agenda; witness El Sayed’s often-repeated refrain “Money out of politics, money in your pocket, Medicare for all.”

While gas and grocery prices aren’t tech related, Big Tech’s enthusiasm for subscription models, algorithmic pricing, and consumer surveillance has contributed to the soaring costs of consumer goods and services. There are forms of tech regulation that can combat this.

For example, New York City mayor Zohran Mamdani implemented a “Click to Cancel” law that 

 to make it just as easy to cancel a subscription as it was to sign up. This is a quick and easy win for any state, as it’s tremendously popular, has immediate benefits, and combats dark patterns and “enshittification” — which consumers loathe. Robyn Caplan, assistant professor of public policy at Duke, explained, “This is a major issue around tech and affordability; it seems minor but rent setting [the use of algorithms to determine pricing in rental housing markets] and subscription traps are terrible for consumers and are a visible marker of the tech industry’s unregulated power.”

The US has no comprehensive privacy law. But Big Tech enables data collection on an enormous scale. From Palantir to Flock, tech companies are taking advantage of the lack of regulations to gather an unprecedented amount of data about Americans. Strong privacy laws are needed to curtail ICE surveillance of immigrants 

through social media data and facial recognition

, the explosion of companies built on the 

collection and monetization of personal and behavioral data

What we do have is the 1974 Privacy Act, which regulates what the federal government is allowed to do with Americans’ personal data. When Elon Musk’s DOGE team took a hatchet to the federal government, it 

 between data collected for one purpose and use for another. In 2025, for example, the Social Security Administration, IRS, and HHS began sharing immigrants’ personal data with immigration enforcement agencies, reversing longstanding policy and facilitating deportation efforts. Nathalie Maréchal, the managing policy director at the Institute for Information, the Internet, and Democracy at Northeastern University, thinks that any reform effort needs to start with accountability. Oversight hearings on privacy that cover DOGE, ICE, and Palantir — which racked up billions in federal contracts in the last two years — could be a first step.

Legislatively, Hannah Bloch-Wehba, a law professor at Texas A&M, has very specific ideas about 

how the 1974 Privacy Act could be revised

. In simple terms, the law needs to be amended to stop agencies from freely sharing people’s data with each other and from circumventing the law by buying data from private companies. She wants an independent watchdog to keep an eye on how the government uses personal information, along with a broader privacy law that limits what companies can collect in the first place.

Federal privacy legislation governing both federal and private collection and use of data would replace an increasingly confusing state-level landscape– twenty-four states have now 

, meaning that some Americans have more privacy rights than others. Many of the experts I spoke to believe that comprehensive privacy legislation is a necessary cornerstone of tech regulation, and should be a top priority for lawmakers.

There’s a creeping suspicion among many Americans that much of the activity on social media isn’t people at all, but bots — the “

.” Bots can be used for spam and fraud, but they can also be used to 

. Social platforms are often slow to take down bots because they inflate user numbers and engagement; if there are no bots on Facebook, it’s unlikely that the platform retains a daily active user number of 2 billion. Caplan says, “We’re not going to stop the dead internet, but we should at least know how much of the internet is dead.” More pragmatically, while the FTC prohibits fake social media influence, it doesn’t require disclosure. Thus, advertisers, many of whom are small businesses, are 

getting charged for bot clicks even when they’re told

To combat this, the FTC could require large social media companies to measure and disclose how much of their traffic is “manufactured activity”: bots, engagement farms, AI agents, and paid humans. Independent third-parties would audit these reports and disclosures. The goal would be transparency: platforms wouldn’t be penalized for having high volumes of bots, but for failing to disclose this. It’s unclear exactly what impact bots are having on social media discourse, but it would be good to know the extent of it.

AI-driven scams are a bipartisan issue: they affect virtually everyone, from teenagers to seniors, and they’re rapidly decreasing trust in platforms, phone calls, text messages, and e-commerce sites. Scams are a hard problem to solve simply because the ecosystem is global, but there is low-hanging fruit that can be tackled immediately.

Ban crypto ATMs. The crypto lobby is rich and powerful, but they aren’t invulnerable: they lost 

$60 million trying to defeat Sherrod Brown

. Crypto ATMS are overwhelmingly used for scams, because once money is transferred via crypto, it’s basically gone forever. The AARP, which has 

strongly advocated for regulation on crypto ATMS

, reports that they were responsible for more than $300 million in scam losses in 2025 (almost certainly a low given that scams are underreported) and that 76% of losses were from people 60 and over. Recently, Coinme was 

 all of its crypto ATMS as part of a settlement against money laundering; this is another quick win that would signal to voters that policymakers are taking scams seriously.

Liability for scam advertising on social platforms. 30% of scams in the US 

 from scam ads. Since self-regulation clearly isn’t working, we have two suggestions. First, make Meta liable for consumer losses due to fraud originating from a Meta ad. Second, require social platforms to implement Know-Your-Customer laws to prevent scam advertisers from creating endless numbers of accounts. This 

 from the Consumer Federation and Consumer Reports outlines these possibilities.

A recent study found that nearly ⅓ of computational papers on social media topics published in top journals 

 companies. The tech companies know a lot more than anyone else about how people use their products — and they’ve vociferously guarded access to their data. Meta’s data access programs for researchers were quietly shut down years ago. In Europe, the Digital Services Act (DSA) requires social platforms to open their platform data vaults to researchers, but in the US, the Computer Fraud and Abuse Act 

 who download (or “scrape”) public website data without permission, and platforms often weaponize their terms of service against researchers, 

 to people conducting public-serving empirical research.

This state of affairs needs to change. Rebekah Tromble, a professor of political science and computer science at Northeastern, said her first priority is enabling “access to company data that allows independent, public-interest researchers to understand the impacts these technologies have on users and society.” Deen Freelon, a professor at the Annenberg School for Communication at the University of Pennsylvania, emphasized the need for data access mandates for very large online platforms, similar to those in the DSA. Ryan Calo, a professor at the University of Washington, added that the US badly needs a way to “protect third-party researchers and whistleblowers.”

Alissa Cooper, the executive director of the Knight-Georgetown Institute, called for restoring funding to the National Science Foundation, the National Endowment for the Arts, and the National Institutes of Health. The Trump administration’s cuts have wiped out entire programs. As Tromble put it, “We need to fund the social scientific and humanistic research necessary to understand societal impacts of these technologies.” That independent scholars can be captured by Big Tech only strengthens the case for public funding of sociotechnical work.

The big picture is that the federal government has operated on austerity budgets, well below capacity, for years — making any big regulatory moves very difficult. This has accelerated during the second Trump administration, which understaffed and under-resourced regulatory bodies by design. Addressing this needs to be a top priority for a Democratic congress.

As Olivier Sylvain, a professor of law at Fordham University noted, lawmakers should “insist on filling vacant positions at administrative agencies with competent subject-matter specialists, particularly at those who've been severely understaffed or brazenly beholden to the White House. The Department of Justice has been debased and demoralized. The Federal Trade Commission currently only has two of five seats filled. The Consumer Financial Protection Bureau has been gutted.” Sylvain served as senior advisor to the chair of the Federal Trade Commission in the Biden Administration.

Similarly, Anna Lenhart, a former senior advisor to the White House Office of Science and Technology Policy and currently a PhD candidate at the University of Maryland, wrote, “Congress needs to reform and expand expert committee staff, who are the staff that lead investigations and write well researched bill text.” Any evidence-based policy reform under a future Democratic president will require a stronger legislative branch, which is hampered by lack of headcount. Lenhart explained, “Committees are currently staffed at 1980 levels, Republicans gutted staff during the red wave in the 90s and we never remedied the situation. Really great, underpaid staff often end up working 16 hour days on end during investigations (including myself).”

A surprising number of people I spoke with mentioned the importance of resurrecting the Office of Technology Assessment, a federal agency that was dedicated to empirical research on the impact of science and technology (

). Founded in the 1970s, the OTA was dismantled by then-House Speaker Newt Gingrich during the “Contract With America” anti-government fervor of the mid-90s, just as the information superhighway was getting off the ground. There is 

, which lawmakers have been pushing to revive since 2019. The 

 seems like a small price to pay for nonpartisan technical research on climate change, social platforms, and artificial intelligence.

This could be paid for by a tax on tech companies. danah boyd, a professor of communication at Cornell, explained, “More than anything it’s about tax law not product. Direct financial implications will prompt more changes.” I know I said I wouldn’t mention AI, but it’s worth noting that Anthropic’s 

 shows the company has paid almost no federal corporate income tax, offsetting its income with billions in loss carryforwards; OpenAI remains private and discloses nothing. Asad Ramzanali, the director of AI policy at the Vanderbilt AI Policy Accelerator, outlined 

34 different taxes that could be leveraged on AI

, including taxes on automation, energy use, and capital gains. Sarah Jackson, associate professor at the University of Pennsylvania's Annenberg School for Communication summed it up: “I just want tech bros to pay their fair share of taxes.”

As civil society organizations, thinktanks, and policy shops scramble to readjust their policy priorities in the wake of this week’s election, it is important to think beyond AI. Expanding regulatory capacity, passing comprehensive privacy legislation, and mandating data access for researchers may not be flashy, but these policies will have real-world implications for technology companies and, potentially, enormous benefits for the average American.

A crowd of supporters surround President Lula holding a microphone at a podium draped with the Brazilian flag.

President Luiz Inacio Lula da Silva delivers a victory speech after winning the presidential runoff in Brazil, Oct. 25, 2026. (AP Photo/Andre Penner)

Lula’s Win Signals Fight to Regulate Social Media Will Escalate in Brazil

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Tech Policy Press is tracking laws and regulations, along with government investigations and litigation, that will shape the rules and accountability for tech companies.

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