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How the US and China Can Step Back from the Battle for AI Model Supremacy

JUNE 16, 2026: A person reads a news report on a smartphone regarding the temporary restriction of Telegram in India ahead of the NEET-UG 2026 re-examination. Authorities said the measure was introduced to curb examination-related fraud, paper leaks and the spread of misinformation during the conduct of the test. (Photo by Waris Nissar/Sipa via AP Images)

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Jenny Kidd & Synthetic Pasts / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/

Intimate Functionality — A Phrase Defining the Next Year of AI Regulation

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Ambiance at the Global Dialogue on AI Governance, Palexpo, Geneva, Switzerland - 07 July 2026.

Global Dialogue on AI Governance Dialogue on AI Governance

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Uncovering the Humanitarian and Nonprofit Sector's AI Governance Crisis

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The US and China are engaged in a battle for AI supremacy, with each country attempting to secure advantage over the other. The upcoming US dialogue on AI governance offers an off-ramp from this escalating conflict that both sides should be eager to embrace.

 "unquestioned and unchallenged global technological dominance,” the US has established a 

 for US AI models, where US authorities use export controls to decide which models are released to the general public, which are banned, and which are accessible just to users approved by the US. At least part of the reason for US officials to use this AI model chokepoint was the suspicion that entities in China could access advanced American models and use them to attack critical US infrastructure.

, requiring its most capable AI models to be closed, instead of the typical practice of Chinese AI companies of allowing open access to model weights, and restricting access to approved users. China’s leaders have parallel fears that the US could use these models to attack their own networks and systems. China already has a pre-deployment review system set up to control AI-generated content and it is easily adaptable to restrict AI models for safety reasons.

Although China has not approved any US models for use in China and US AI companies officially do not allow access by Chinese users, US models are 

 among Chinese researchers and company experts through overseas proxies, a practice implicitly tolerated by Chinese authorities. But recently, these authorities have instructed Chinese users to uninstall and avoid models from the American AI company Anthropic, citing 

 that allow US surveillance of user activity. This seems to be just the beginning of a more serious crackdown on the availability of American AI models in China.

For their part, US authorities are moving toward blocking US government agencies and companies from using Chinese AI models. An ongoing 

 by two House committees focuses on the national security and cybersecurity risks from Chinese-developed AI models. US authorities have 

 by Chinese developers. The State Department has even 

 around the world to trumpet the dangers of using Chinese AI models. The Defense Department and other agencies already 

 the use of Chinese models in government systems, and some state Attorneys General are 

 Congress to ban Chinese models on all government devices and systems, citing restrictions on TikTok as precedent. Industry analyst Paul Triolo 

 that a possible executive order restricting the use of Chinese AI models could be based on federal procurement and government-use prohibition, a technology supply chain restriction, a national emergency order under the International Economic Emergency Powers Act, a prohibition just on critical infrastructure and high-risk uses or through federal contracting leverage. The writing is on the wall, and only a change in direction can prevent the move to a ban.

These bans on rival countries’ most capable models would be costly. Chinese open AI models are 

 Alibaba’s Qwen for one of its customer service applications. Perplexity also 

 the coding application Cursor, the delivery company 

 and the legal services firm Harvey. Industry analyst Paul Kedrosky 

 that Chinese open models now have more than a third of enterprise use of AI models through OpenRouter, a service that provides developers with access to a range of models, so a ban on their use “would affect real US enterprise workflows, costs, and vendor architecture.” The US startup market is even more dependent on Chinese AI. Over a year ago, a partner in the venture capital firm Andreessen Horowitz 

 there was an "80% chance" that US startups were using “a Chinese open-source model.”

But without security guarantees, cross-border access to AI models cannot continue. If the US thinks Chinese systems are unsafe, leading to cybersecurity threats, dangerous bioweapons or a loss of human control, the US won’t allow access to them and US consumers and companies will be worse off. And similar security concerns will push China toward a ban of US AI models, even for research and development purposes.

Of course, the US might want that as a way to protect US AI companies, in the same way that the US protects US automakers by imposing prohibitively high tariffs on the affordable, environmentally-friendly high performance electric vehicles coming out of China. This protectionist impulse could drive US policymakers to attempt to organize a world-wide boycott of Chinese AI models, the way they did to drive Huawei equipment out of allied telecom and computer networks. But given the recent surprise revelation of an effective 

 through export control directives, it will be hard to convince other countries to give up all reliance on Chinese models, when US AI models might vanish overnight for undisclosed security or foreign policy reasons.

So that is the choice for US policymakers. An explicit protectionist policy for US AI models, leading to a hard-to-secure and even harder-to-enforce global boycott of Chinese AI models, versus a cooperative strategy of reducing AI risks for all providers of AI models, allowing global AI competition on the merits.

The key to a cooperative approach is pre-deployment review of models for agreed-upon problem areas including cybersecurity, bioweapons and loss of human control. This is the general approach taken in several 

, titled the “Great American AI Act.” Some neutral technical standards body would set standards for AI risk reduction in these three areas, allowing authorized auditors to test AI models against these standards, giving rise to a certification. Crucially, this testing regime would apply to 

 AI models, including open source Chinese models. The US could enforce compliance for open source models by requiring entities that distribute AI models (like cloud providers, GitHub, or Hugging Face) to ensure foreign-origin open models pass specific US safety certifications before hosting them. China might be encouraged to adopt a similar safety regime, or piggy-back off the US system.

The first steps toward such a system could be made with confidence-building measures at the 

 that might commence just before the World AI Conference in Shanghai, now set for July 17-20. The dialogue could begin with information sharing about AI risks and methods of assessing and mitigating them, with no expectation at this stage of reaching any agreement. But the AI dialogue could be a step back from the brink of all-out battle for AI supremacy, with the US and China both choosing the path of safety and cooperation, not security and confrontation.

 last month as workers demand job protections against the South Korean carmaker’s plans to deploy humanoid robots on factory floors.

“Not a single robot” will be allowed onto the assembly floor without a labor-management agreement, the union 

The dispute reflects a broader struggle unfolding across Asia as artificial intelligence reshapes workplaces and raises questions over who should benefit from productivity gains. In South Korea, unions at technology companies that have benefited from the AI boom are demanding larger bonuses and a greater share of corporate profits.

Major semiconductor companies, including SK Hynix and 

, have already agreed to record-high payouts, with some memory chip workers expected to receive bonuses up to $416,000.

But the debate extends beyond compensation. The 

 have called on companies benefiting from AI-driven profits to share gains not only with their own employees but also with workers in different departments, suppliers and subcontractors. Samsung union’s own win has 

. Those in less lucrative sections received much smaller bonuses and, in protest, left the union in droves.

Across Asia — from South Korea to Singapore, China and the Philippines — workers and governments are taking steps to respond to AI’s impact on employment. The approaches vary widely depending on each country’s political system, labor institutions and stage of AI adoption.

 dismissed after employers considered replacing them with AI. In Singapore, 

 laid-off workers access retraining programs. In the Philippines, where business process outsourcing (BPO) workers largely lack a union, workers are 

“Before, it could be predictable. Maybe 10 years later, we still have this job. But (now) we cannot expect our existing job to really exist in the next two or three years,” Kan Matsuzaki, assistant general secretary at global union IndustriALL, told Tech Policy Press. "This is why I think the government, the employer, and the union should have a proper social dialogue.”

“This is a question about democracy and also freedom of association,” he added.

 for an AI transition that is just to workers, which cover transparency in algorithmic management and data privacy; developing quality jobs and re-skilling programs, occupational health and safety, progressive AI taxation, as well as rights to organizing and collective bargaining.

“Whether it's AI-based or not, US employers have always enjoyed a great deal of freedom in firing workers,” Kim Keechang, a scholar at the Korea University School of Law, told Tech Policy Press. “So this discussion only makes sense in countries where employees have traditionally enjoyed a very strong legal and institutional protection of job security.”

AI profits are fueling a new labor fight in South Korea

South Korea’s current labor push reflects a broader shift in the country’s balance of power between companies and workers. The country’s strong unions, a pro-labor government and legal changes that require companies to negotiate with workers have created conditions that are unusual compared with many other economies. But that influence was not always guaranteed.

For years, South Korean unions were actually losing leverage as companies challenged union leaders through lawsuits and courts increasingly favored employers, said Kim. Some union leaders faced financial ruin, and some 

 under the pressure. At the same time, companies increasingly relied on subcontractors to avoid some of the obligations associated with South Korea’s strict labor protections, which make dismissing permanent employees difficult.

The balance began to shift in 2025, when South Koreans elected the center-left Democratic Party of Korea and President Lee Jae-myung, who worked in a factory as a teenager before entering politics. A major labor reform was the passage of the so-called 

, which took effect in March. The law expands collective bargaining rights for subcontracted workers and limits companies’ ability to pursue large damages claims against unions. It was named after the yellow envelopes supporters used to send donations to union members facing legal costs.

The law’s implementation quickly changed the labor landscape. On the day it took effect, some 

 sought wage negotiations with management.

Around the same time, Samsung workers pushed for a larger share of the company’s gains from the global AI boom, which has driven demand for memory chips. Samsung unions 

threatened an 18-day strike by 48,000 workers

 if the company didn’t agree to share higher bonuses. The negotiations, which lasted through the night, were mediated by the labor minister Kim Younghoon, a 

, who Kim believes contributed to the workers’ massive win.

The agreement strengthened unions’ confidence across South Korea, but their demands now extend beyond pay. “As they insist that the introduction of an AI-based solution would require employees' consent,” Kim said, unions are increasingly seeking influence over how companies deploy new technologies in the workplace.

That demand puts South Korea at the center of a growing global debate over AI governance: whether workers should have a formal role in decisions about automation, algorithmic management and workplace surveillance. The country’s approach also highlights a fundamental difference between labor systems. In places where workers already have strong legal protections and collective bargaining rights, the debate often centers on how AI gains should be shared. In countries with weaker labor institutions, the focus is more often on preventing job losses and creating new protections.

Singapore bets on adaptation as workers face AI disruption

South Korea’s approach is difficult to replicate elsewhere in Asia. The region’s governments and labor movements are responding to AI disruption through very different political and economic systems.

In Singapore, where many global tech companies’ Asia headquarters are based, the scale of layoffs remains small compared to the overall economy. But the country is already preparing for what policymakers see as inevitable.

“The general approach in Singapore by all parties is to accept that AI is likely to significantly change work and the labor market, and that it's necessary to adapt and take advantage of the changes rather than resist them,” Walter Edgar Theseira, associate professor at Singapore University of Social Sciences, told Tech Policy Press.

Unlike in South Korea, where policymakers have debated whether companies benefiting from AI should share more of their gains with workers, the Singaporean government would be “reluctant” to impose something similar if it risked discouraging investments or business transformation. “What the government doesn't want is a situation where firms leave Singapore,” he added.

Still, recent layoffs at major technology companies have increased anxiety among workers, particularly those in white-collar roles. 

 — which owns Southeast Asia’s biggest e-commerce Shopee — and rival 

 have all announced job cuts in recent months.

“The changes we are implementing vary by team and include layoffs, open role closures, and moving thousands of employees to business-critical priorities across the company,” a Meta spokesperson told Tech Policy Press. Sea and Lazada did not respond to requests for comment.

The layoffs have prompted new community efforts to support affected workers. SG60 Voices from the Heart, an informal community that organizes sharing events in Singapore, created two separate gatherings focused on retrenchment, with a third planned for July.

 drew 50 participants aged 35-45 from different industries, including a government official who was keen to better understand the situation on the ground, Sebastian Chen, SG60 Voices from the Heart co-founder, told Tech Policy Press.

“It's like an informal mental health group or collective,” Chen said. Chen said one noticeable shift was that layoffs were no longer limited to employees perceived as underperforming. Capable workers, including human resources professionals, were also being affected, creating uncertainty among people who still had jobs. While AI is one factor contributing to these changes, Chen said many workers view it as only part of a broader restructuring trend.

“Actually, the sentiment is that AI is just an excuse,” he said.

The anxiety among Singaporean workers contrasts with the more confrontational labor response in South Korea. “Generally, all parties accept that resisting changes that would result in a reduction of competitiveness for the economy and workers here is self-defeating," Theseira said.

Instead, the focus in Singapore from a policy perspective is to view “it as an economic transformation challenge” through solutions like investments in lifelong learning via the SkillsFuture program, under which AI courses have seen recent spikes in enrollment, mid-career support programs, career conversion pathways, and workforce transformation initiatives, according to Ives Tay, Independent Skills & Workforce Consultant in Singapore.

“At the same time, tripartite collaboration between government, employers, and unions has helped create a relatively coordinated response to technological disruption,” Tay told Tech Policy Press.

Singapore’s unions continue to support laid-off members by ensuring retrenchment benefits align with collective agreements negotiated with employers. But union membership has traditionally been concentrated among blue-collar workers. Technology companies such as Meta and Sea do not have unions, though interest among white-collar workers 

 as layoffs have become more common. The National Trades Union Congress has begun outreach efforts targeting these workers, including helping some nonmembers negotiate retrenchment benefits.

But skills training and severance pay only go so far. “We need stronger links between skills acquisition, job redesign, and wage growth,” Tay said. “Small and medium-sized enterprises often lack the resources to redesign jobs around AI.”

The employment data in Singapore, however, is not telling the whole story. “Companies have incentives and policy regulations that require them to favor local employment, so the impact of AI restructuring is likely to show up first in reductions or moderations of foreign employment,” Theseira said. Besides, there’s been a rise “in the use of AI to replace certain outsourced business services, such as software development and maintenance done outside of Singapore.”

Philippine BPO workers face AI disruption without strong unions

The challenges facing workers in Singapore are amplified in the Philippines, where millions of workers depend on the business process outsourcing (BPO) industry and many lack the collective bargaining protections available in countries such as South Korea. Quality analysts, workers who evaluate the quality of customer support calls, “are the first line of BPO workers' jobs to be affected by AI integration,” Renso Bajala, Secretary General of BIEN (BPO Industry Employees Network), told Tech Policy Press. The BPO industry does not currently have a union, and terminated workers have no benefits.

, a campaign to raise awareness about the threats of AI integration. One of the campaign’s key messages is that workers should not bear the entire responsibility for adapting to AI.

“Upskilling should not be a burden for the Filipino workers, so it should be the responsibility of the stakeholders such as the company and the Philippine government,” Bajala said.

For Matsuzaki of IndustriALL, the disruption facing tech workers is part of a longer history. Blue-collar workers have experienced similar pressures from automation, globalization and supply chain shifts for decades.

The challenge, he said, is ensuring that workers have a meaningful role in shaping technological change rather than simply absorbing its costs.

Companies will continue to seek ways to reduce costs, including resisting unionization efforts or moving operations to jurisdictions with fewer labor obligations, Matsuzaki said.

“We cannot stop those changes,” he said. “What we need is a strong trade union movement. Solidarity is very much important.”

As AI adoption accelerates, Asia’s response is revealing different models for managing technological disruption. South Korea is pushing toward stronger worker participation and collective bargaining over AI deployment. Singapore is emphasizing adaptation through training and economic transformation. The Philippines highlights the risks faced by workers without strong labor institutions.

The debate is ultimately not only about whether AI will replace jobs. It is about who has a voice in deciding how workplaces change — and who benefits from the productivity gains that follow.

On June 16, the Ministry of Electronics and Information Technology (MeiTY) 

 Telegram to be blocked across India, invoking 

 of the Information Technology Act, 2000, over fraud on the platform 

 to a national medical entrance examination. Three days later, in 

 the platform's challenge, holding the block was proportionate according to the legal framework laid down in a prior ruling, 

Two pieces on Tech Policy Press have already examined that finding and found it wanting.

 that reading an entire platform into the statutory term “information” normalizes platform-wide blocking as a default tool. Apar Gupta 

 on Telegram's technical architecture inverted the burden proportionality is meant to impose. Both treat this as a proportionality failure. That framing is correct as far as it goes, and incomplete in a way that should matter more than either piece allows for.

Proportionality is a second-stage inquiry. It asks whether a restriction is a necessary, least-intrusive means to a legitimate end. It does not ask whether the end invoked was the correct one to begin with. Justice Karia's judgment spends four paragraphs (¶¶38–41) working through the 

 proportionality checklist—appropriateness, necessity, least restrictive measure—and finds the Telegram block satisfies all four. What it never does, and what neither the Union of India’s submissions nor the 

 attempt, is establish that “public order” was the right label for what the paper-leak fraud on the NEET UG 2026 exam actually threatened. The order simply asserts it. Justice Karia held that the “underlying object” was to avert disruption of public order (¶40) and further noted the Committee found narrower takedown measures ineffective. (¶45) At no point does the Delhi High Court’s 

 pause on whether examination fraud, however serious, rises to the constitutional threshold “public order” describes, as opposed to the threshold “law and order” describes. That omission, not the proportionality analysis built on top of it, is the judgment's fundamental defect.

Indian constitutional doctrine has an answer to this question, and it predates the internet by half a century. In 

, a Constitution Bench of the Supreme Court distinguished detention orders citing “law and order” from those citing “public order,” holding the two are concentric circles of differing radius rather than interchangeable labels, security of the state at the center, public order around it, law and order the widest ring. 

 supplied the test for telling them apart, asking whether the act disturbs the even tempo of the life of the community, or merely injures a particular person or defined class without reverberating outward into general disorder. An act can be a serious criminal wrong and still fail that test.

It is fair to ask whether a test built for preventive detention under 

 travels intact into a blocking order made under 

. It does, because the legal vocabulary does the traveling for it. The 

Section 7 of the Indian Telegraph Act, 1885

2009 Blocking Rules [See Form- Question 17(v)]

 both use the words public order and security of the State without redefining them for the digital context, and 

 measured internet shutdowns against that same borrowed vocabulary rather than inventing a separate one for the online sphere. If public order were meant to carry a lower threshold when a platform is blocked than when a person’s liberty is restrained, that would need to be argued and defended on its own terms, not assumed by using the word and moving on. Neither party in Telegram FZ made that argument, and the Court therefore had no reason to test it.

 to MeitY, reproduced in the Final Order, identify channels operating under names such as “PAPER LEAKED NEET” and “Re-NEET 2026,” extracting money from candidates and families in exchange for supposed access to leaked material. That is extortion and fraud directed at an identifiable class—examination candidates and their families—through identifiable channels. It is not, on the face of the material recited in the judgment, an act with the potentiality 

 require to disturb the even tempo of the community at large. The judgment does not argue otherwise. It does not argue the point at all. It moves directly from “misuse of the platform” to “public order situation” as though the two were self-evidently the same thing, and having made that leap, spends its analytical energy entirely downstream, on whether the resulting block was proportionate.

A rationale that has lost this argument before, informally, every time

The claim that examination integrity justifies extraordinary suspension powers is not new to Indian internet governance. The 

 internet service in 2016; Rajasthan, which 

 in February 2018, twice more that year and again another examination in 

 internet services every day its higher education examinations were concluded.

This is not an aberration state governments occasionally reach for. This standing administrative reflex, repeated across four states over six years, entirely outside anything 

It was also, at least once, formally renounced. In 2018, the Rajasthan shutdown was 

 before the Rajasthan High Court. The State Government 

 an Affidavit before the Court that no order suspending internet services would be 

Dhirendra Singh Rajpurohit v State of Rajasthan

 of as infructuous on the strength of that undertaking. Three years later, for 

 the internet down again, in more districts than in 2018, with no official consequence for violating an affidavit before the Constitutional Court.

There is a further detail that goes directly to the threshold question raised above. The suspension orders do not strictly invoke “public order” at all. Read them and the recurring phrase, across Rajasthan in 2018 and again in 2021, is that suspension is necessary “to maintain law and order” in the district or division concerned, language the district magistrates and divisional commissioners issuing these orders used deliberately, since the 

 themselves list public order as a separate, narrower ground. The Executive’s own contemporaneous description of this harm—rumor, paper leaks, exam-fraud gangs operating through mobile internet—has for six years been law and order, the wider and less demanding of the two categories 

 identifies. MeitY's Telegram order breaks with that settled administrative self-description without acknowledging it, reclassifying the same genus of harm as public order for the first time, at the one moment the claim finally reached a forum with the authority to make the reclassification binding.

The clearest evidence that the judgment’s cost lies less in the blocking power itself than in the reasoning it left unexamined arrived within a fortnight. On July 1, MeitY 

 it to defer the rollout of WhatsApp’s usernames feature, 

 in India pending consultations, and to explain within three days why regulatory action should not follow under the 

 that it draws directly on arguments the government made in the Telegram litigation itself, that username-based communication conceals identity, that a single account can spin up dozens of bots, that mirror accounts can be recreated within minutes of enforcement action. These were Solicitor General of India’s 

 to Justice Karia, describing why Telegram’s architecture made narrower measures futile. They have now been redirected at a platform that has not been found to have hosted a single fraudulent channel, to justify pausing a feature before it has launched.

What makes this different in kind, not just in its target, is that it happens entirely outside the framework 

, no interim direction confirmed by a final one. There is a notice—on uncertain statutory footing—backed by the threat of unspecified “regulatory action” if the explanation offered does not satisfy the Ministry and hence has no clear basis in law and amounts to a product approval regime no statute authorizes. Whether or not that characterization survives Meta’s response, the sequence is instructive on its own terms. A blocking order for which the “public order” finding went untested has, in under three weeks, supplied the vocabulary for an intervention that does not even attempt the procedural discipline, thin as it was, that 

 requires. The architecture-as-danger framing outlived the case that produced it and is now doing work in a register where nobody has to justify it to a court at all.

There is a further problem with the causal story the block rested on. NEET UG was not the last examination compromised. On June 27, Maharashtra police 

 a leaked Maharashtra Teacher Eligibility Test paper hours before the exam was due to begin, forcing the State to postpone it statewide, with the investigation 

 the breach to a private printing press in Agra that has produced the state’s papers for close to two decades. Two days later, Jaipur police 

 a cheating racket running through a Rajasthan Paramedical Council examination center, organized through invigilators paid in cash to seat and assist particular candidates. Neither leak travelled through Telegram or any other app. Neither needed to. The mechanism the government told Justice Karia 

 reaching millions of downstream users within minutes- was never the only route, or even the primary one, by which examination integrity in India actually fails. Printing presses, transport chains and paid exam proctors require no platform architecture at all. A government that shut down one distribution channel while the leak itself kept occurring upstream of any platform, in the same fortnight, was not addressing the harm it named. It was being seen to act on a deadline that had already passed.

None of this bears on the threshold question directly. It shows that even bracketing that question and meeting the judgment on its own proportionality terms, the causal premise was already false. 

 have supplied one for sixty years, and it has simply gone unargued at the one moment its application would have mattered most, a reasoned High Court judgment concerning a platform used by roughly 150 million people in India. The 

 did not ask the threshold question. Neither of the parties pressed it either, arguing proportionality rather than antecedent legitimacy. The Court, accordingly, never needed to answer it. That is how a claim with an eight-year losing record acquires precedential weight without ever being made to defend itself on its own terms. The WhatsApp 

 is what happens next once it has. Examination fraud is a real and serious harm. Whether it is a public order harm remains, formally, an open question for Indian courts.

Have you ever met anyone who thinks cookie banners are a good idea? I haven't. Every day, they remind Europeans that something isn't working in the EU's approach to digital regulation. Yet on June 18, EU member states, negotiating in the European Council, proposed rejecting the 

European Commission's Digital Omnibus proposal

 to eliminate them. The Commission's idea is straightforward: give people a browser setting that lets them choose their cookie preferences once, instead of forcing them to click through banners on every website.

reportedly including Germany, France and Poland

 – scrap the European Commission’s highly promising legislative proposal?

What seems to sway the opinions in the European capitals is fear. Concretely, governments fear that restrictions on cookie pop-ups would further reduce advertising revenues for news and media companies already struggling for funding. This fear, in particular, has 

, which warned member states that browser-level consent would cause advertising revenues to collapse in the EU, undermining its sovereignty. After all, by giving users meaningful control of their EU data rights, news and media companies might find it difficult to show users lucrative online ads.

The concern is understandable. Independent news and media are essential pillars of European democracy, and their financial sustainability deserves serious protection. More than three decades after the arrival of the Web, advertising remains a crucial source of revenue for many publishers. Yet much of the digital advertising market has been captured by large US technology companies such as Google and Meta, leaving European media organizations increasingly dependent on platforms that control the rules, infrastructure, and data that underpin online advertising.

These fears are likely reinforced by the fact that the technology – “automated and machine-readable indications of data subject’s choices” 

 – sounds like a complex, difficult beast. Except that it isn’t. And most stakeholders – in private – agreed on that when 

my academic colleagues Sebastian Zimmeck, Harshvardhan J. Pandit, Frederik Zuiderveen Borgesius, and Cristiana Teixeira Santos

 brought participants from industry, civil society and academia together in Brussels for a one-day workshop in May 2026.

 on what such a technology would look like. California has already proved that it works well, with a technology called the “

” (GPC). Since 2020, the GPC has allowed users to choose in their web browsers whether they want their data to be sold or shared with third parties that they do not know. 

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