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Democrats Target AI, Data Centers, Elon Musk and Big Tech in Midterm Victory Speeches

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A digital collage combining a historical painting of a miner with a large grid of small online images. At the center, a muscular miner works underground with a heavy drill inside a dark tunnel. To the left and along the bottom, hundreds of tiny square images form a dense mosaic, suggesting scraped internet content. A bright beam of light connects the image grid to the miner’s drill. On the right, a block of colorful digital noise contrasts with the detailed painting, reinforcing the link between physical mining and data extraction.

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October 2026 US Tech Policy Roundup

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After United States midterm elections in which Democrats posted 

, candidates took the stage to deliver victory speeches. Consistent with the salience of AI and data centers in the political discourse, concerns over the influence of tech oligarchs such as Elon Musk, and the role of Big Tech in shaping the information ecosystem and the economy, many candidates named these issues directly. Some also complained about AI deepfakes.

What follows is a sample of such statements from races in a variety of jurisdictions.

Courtney C. Radsch, PhD, is a journalist, scholar and human rights advocate who currently directs the Center for Media & Digital Governance at Open Markets Institute and is a non-resident fellow at the Brookings Institution. She serves on the board of Tech Policy Press.

 and across state offices represent more than just an opportunity to stave off democratic decline. They are also an instructive referendum on the power of Big Tech and AI, and the broad desire among the electorate to hold tech to account.

Concerns about unfettered power of tech firms and their aggressive AI vision have propelled a 

 against the infestation of data centers in communities around the country, fueled the movement against 

 and the concentration of power in a handful of Big Tech firms, and underpinned discontent with the vast corruption and collusion between the political class and the Trump administration in particular.

When the new Congress takes office in January, the question is: will this majority confront concentrated corporate power and technofascism—or simply oversee its continued expansion?

AI governance, Big Tech accountability, monopoly break-ups, media plurality and press freedom belong on the same agenda.

The states have already shown where popular will is headed in terms of putting limits on permissible types of 

Each of these ongoing regulatory dramas is an expression of the same underlying failure of Congress do its job on behalf of the American people. Policymakers have stood by as profit-maximizing corporations built business models on the commercial exploitation of personal and commercial data, rewarding social and psychological manipulation, and

There remains a massive legal vacuum where basic protective frameworks like fiduciary duties, liability standards, enforceable transparency and privacy safeguards simply do not exist that the next Congress can now address. It must take action to reinforce these efforts at the federal level. 

Congress must also mitigate concentration of power over our information and communications system, from 

 at the root of AI training and inference and 

, we cannot let the transition to AI entrench the same gatekeepers under new business models. Independent journalism must have the power to negotiate with tech firms through collective bargaining, statutory licensing and enforcement of copyright and antitrust law, and not merely the option to accept whatever terms platforms offer.

Understanding the power that the new majority in Congress offers a useful framework for turning this opening into action — check out the 

We Build Progress explainer on majority power

Use hearings and lawful subpoenas to demand answers from both corporate executives and public officials and hold them accountable. The agenda should include: Examining media consolidation and editorial retaliation by the Trump administration and formerly independent regulators that are supposed to provide oversight. Scrutinizing media mergers and investigating whether regulatory or licensing powers are being used to pressure independent reporting. Investigating exclusionary deals, lobbying and political influence by Big Tech and its AI brethren.

 Make room for sustained scrutiny of AI — both frontier and sectoral AI — information markets and communication systems to avoid another cycle of CEO appearances with no follow-through.

: Choose leaders prepared to challenge corporate gatekeepers, including when their influence reaches inside the Democratic Party, and resist the temptation to rely once again on Big Tech largess. The people voted for accountability; Democrats should provide it.

Put enforceable AI safeguards including related to design and liability; stronger competition protections; updated protections against media consolidation that accounts for Big Tech players in streaming, TV and cloud; and proactive protections for workers, including information producers and the creative sectors, as well as advertising in AI on the voting agenda. Reject efforts to trade away state AI protections for industry-friendly federal inaction.

Specifically, Congress should investigate reports that Meta interfered in democratic elections in the US (for example, 

 Indivisible’s accounts during national No Kings protests) and Brazil (Meta faced 

, that it favored President Lula da Silva’s challenger in the recent elections by suspending Lula's campaign advertising account, suppressing posts, and blocking AI-generated content). Congressional investigators should do 

 into how Big Tech and AI companies politically interfere in or manipulate access to information and discriminate against specific types of accounts or content based on 

 from independent, non-partisan experts and force executives to testify under oath.

Congressional leaders should also take full advantage of their public platform to elevate oversight, press freedom and democratic accountability rather than to confer prestige on those seeking to escape scrutiny, as has been the pattern over the past few years.

A majority is not a magic wand, but at least it’s a check on unfettered one-party rule. The 

 and presidential veto remain real constraints on legislation and will undoubtedly be deployed in Trump’s remaining lame duck term. Congress does not itself decide individual merger cases, and the Supreme Court 

 the role of independent agencies originally set up by Congress. But those limits are no excuse to abandon oversight, agenda-setting or the work of building stronger democratic protections and passing new laws where needed.

Winning a majority opens the door. The extent to which Democrats effectively wield public power to disperse private power, break up Big Tech’s influence over the information and communications ecosystem and defend an independent press is the test that will determine if this shift in power will curb rising autocracy and technofascism in America.

Parents are underestimating how frequently their children use generative artificial intelligence, especially at younger ages. According to a 

, 40% of French 11- and 12-year-olds report using general-purpose generative AI at least weekly, compared with just 26% according to parents’ own estimates.

More strikingly, among the 11- and 12-year-olds who use generative AI, 14% say they turn to these tools to confide in them. These findings illustrate how generative AI is becoming part of young people's lives in ways that go well beyond homework assistance or information searches, while parents are not necessarily equipped to support them in navigating these new experiences.

They also come at a particularly timely moment for European regulation. In September, the European Commission presented its 

, a proposal introducing obligations across a range of digital services, including social media, video-sharing platforms, online games, and AI chatbots and companions.

But while the Commission has proposed a clear minimum age for social media access, its approach to AI chatbots and companions is notably different. This raises an important question: how well does the EU's emerging regulatory framework reflect the ways children actually use these technologies, and the risks they may encounter?

Children are already growing up with generative AI

Between June 24 and 30, YouGov surveyed 970 French children and teenagers aged 11 to 17, alongside 1,138 parents of children in the same age range. The study examined their use of generative AI, including general-purpose chatbots such as ChatGPT, AI companions such as Character.AI, AI embedded in social media such as My AI on Snapchat, and voice assistants such as Siri.

One finding stands out: generative AI use is already widespread, including among the youngest respondents. Perhaps more surprisingly, 23% of children in the 11-12 age group report regularly using generative AI embedded in social media, while 16% regularly use AI companions, despite most of these services' terms and conditions restricting access under the age of 13.

For teenagers, generative AI is primarily a practical resource, helping them find information, save time, learn and complete schoolwork. Among those familiar with it, 89% identify at least one benefit. Parents do recognize its potential, but they also express concerns, particularly about children's ability to think independently, the reliability of AI-generated information and the protection of personal data. Moreover, 44% of parents whose children use generative AI say they do not feel at ease with that use.

The findings therefore paint a more nuanced picture than one of children simply being exposed to a new category of online harms. Generative AI is already serving different purposes in their daily lives, some beneficial, others potentially concerning. Understanding these differences is essential to developing an appropriate regulatory response.

Not all AI interactions are created equal

Another striking finding concerns the time teenagers spend interacting with these systems.

Among surveyed teenagers who use generative AI, 92% report spending more time than they would like on at least one of the tools covered by the study. This feeling is particularly pronounced among users of AI companions (59%) and generative AI embedded in social media (57%), compared with 45% for general-purpose generative AI users.

These figures should not be interpreted as evidence of addiction. Spending more time than intended is a subjective experience, not a clinical diagnosis. Nevertheless, the differences between categories suggest that the design and purpose of these systems deserve closer attention. In particular, AI companions are explicitly designed to sustain ongoing conversations, often through personalized interactions that can resemble human relationships. AI embedded in social media, meanwhile, operates within environments whose business models already rely on encouraging repeated and maximized engagement.

The survey also reveals a relationship worth investigating between generative AI use and emotional well-being. While 38% of all surveyed teenagers report experiencing negative emotions at least weekly, the proportion rises to 50% among regular users of AI companions. Of course, correlation does not imply causation. It may be that teenagers already experiencing emotional difficulties are more likely to turn to conversational AI for reassurance or companionship. This uncertainty is precisely why further research is needed.

The KIDS Act's uneven approach to age restrictions

So how does the EU KIDS Act address these different uses and risks?

One of the proposal's main additions to the existing EU digital rulebook is a three-tier system for accessing social media and video-sharing services presenting certain risks. Children under 13 would generally be prohibited from holding accounts on these services, those aged 13 and 14 could use limited accounts supervised by a parent or guardian, while teenagers aged 15 and above could open their own accounts.

AI chatbots and companions, however, are treated differently. Although the proposal does not establish an equivalent general prohibition on access for children under 13, Article 14 requires their access to be enabled and controlled through parental tools. These services are also subject to specific safety-by-design obligations, including measures addressing potentially addictive designs and emotionally dependent relationships.

The reliance on parental controls deserves scrutiny. According to the survey, only 32% of parents feel sufficiently supported in helping their children navigate generative AI, while 42% report receiving no support at all. Parental controls can be useful, but policymakers should take into account that parents do not always have a clear picture of how their children use these technologies. The primary responsibility for mitigating risks should remain with the companies designing the systems.

This difference in treatment is particularly interesting in light of another of the survey’s key findings. Younger children already report using generative AI for emotional support, while users of AI companions are especially likely to feel they spend more time on these systems than intended. The KIDS Act does not overlook these risks. 

 introduces specific obligations for AI companions and conversational chatbots. Providers would have to avoid designs that simulate interpersonal relationships in ways likely to create emotional dependency, refrain by default from using information derived from previous interactions in subsequent conversations, and conduct risk evaluations before placing their systems on the market.

However, the difference in the treatment of access remains difficult to ignore. Why does the KIDS Act prohibit children under 13 from holding accounts on certain platforms, while allowing them to access AI companions under parental supervision? There may be legitimate reasons. Social media and conversational AI present distinct risks, and the same safeguards will not necessarily be appropriate for both. But those distinctions should be supported by evidence, not assumptions about which categories of technology are inherently more dangerous.

The boundaries are also becoming increasingly blurred. Generative AI is now embedded in social media services, meaning that children may encounter similar conversational functionalities across different types of platforms. The KIDS Act recognizes this convergence, introducing additional safeguards for AI companions and chatbots embedded in social media, video-sharing services and online games, including restrictions on their automatic activation. Nevertheless, the differences in age-based access requirements raise questions about how consistently the proposal addresses risks associated with similar functionalities.

What this means for the KIDS Act negotiations

The survey offers several lessons for European lawmakers as negotiations on the KIDS Act get underway.

First, policymakers should pay greater attention to the characteristics of AI systems that encourage prolonged, personalized or emotionally significant interactions. Article 14 provides an important starting point, but the effectiveness of its safeguards will depend on how risks are assessed, how compliance is demonstrated, and whether enforcement authorities have the expertise needed to evaluate these systems.

Second, the EU should ensure greater consistency in how it approaches risks across different categories of digital services. Age restrictions may be justified in certain circumstances, but they should be proportionate and grounded in evidence. More importantly, restricting access should not become a substitute for addressing the features that make a service potentially harmful.

Third, policymakers need to invest in independent, longitudinal research examining how children's interactions with generative AI evolve over time, particularly among younger and potentially more vulnerable users. The current evidence remains limited, and self-reported surveys such as this one cannot establish causal relationships between AI use and well-being. Still, the absence of conclusive evidence should neither justify inaction nor serve as a blank cheque for sweeping restrictions.

Finally, regulation must preserve the opportunities generative AI can offer children, including learning, creativity and access to information, while ensuring they are not left alone to navigate potentially harmful systems.

The KIDS Act represents an important opportunity to move European child-safety regulation beyond managing access and towards greater accountability for digital product design. Its success, however, will depend on whether its provisions adequately reflect children's actual experiences. The fundamental question is not simply at what age children should be allowed to use a particular technology, but under what conditions that technology can be made safe and beneficial for them. If the EU wants to develop an effective framework for protecting children in the age of generative AI, that is where the debate should begin.

Alissa Cooper is the executive director of the 

 all announced plans to support a new mechanism that lets web publishers express their desire to opt out of having their content used to train AI models. The 

, authored by Anthropic’s Kevin Kelley, attracted considerable attention across the tech press (see 

, with the coverage highlighting the prospect of giving publishers greater control over how AI companies use their content.

But the announcements — and much of the subsequent coverage — leave out important context about the relationship between the product changes, ongoing standardization activity, and the policy environment. Understanding these nuances requires looking at the ongoing standards process and the broader policy debates surrounding AI, search, and competition.

The new proposal comes after years of disputes between publishers and companies that crawl the web — in court rooms, industry standards bodies, and public forums. Nearly two years ago, the Internet Engineering Task Force (IETF), the premier internet standards development organization, launched an effort known as 

 (“AIPREF”) to develop new standards that would, among other things, give websites expanded options for automatically expressing preferences about how their content gets used for AI-related purposes. This work is meant to build in part on a prior IETF standard, 

, which specifies the format for the commonly used “robots.txt” files employed by websites to control crawlers. Participants from Anthropic, Google, and Microsoft have all been actively involved in the AIPREF group, while OpenAI contributions have been minimal, despite the company stating last week that the announcement “strengthens our continued participation in the IETF standards discussions.”

This AIPREF work was sparked in part by the process of developing the 

, a voluntary compliance mechanism under the EU AI Act. Is signatories (most of the largest American and European AI model companies) commit to abide by the machine-readable preferences that sites express.

When looking beyond the headlines, what’s most important to understand about these announcements?

1. The mechanism is not an official “standard” — but adoption by the four largest search and AI providers might make it a de facto one.

Last week’s proposal has no official status in the IETF. It will need to be discussed and debated (probably at some length) to see if it will move forward toward standardization. But when the largest companies deploy support for it (all have committed to do so by 2027), that standardization status may not matter. If site operators are convinced to change their robots.txt files to comply with the new proposal, it may become the de facto way of signaling AI training preferences regardless of where the standards end up.

The proposal announced on October 30 (the “policy token draft,” for short) is documented in what is colloquially known as an “individual draft” — a submission to the IETF made by an author, which has no formal standing of any kind in the standards process. Anyone can submit an individual draft to the IETF, and over 1,000 are submitted each year.

The AIPREF working group already has two other drafts that are further along in the standards process, one that expresses a vocabulary for AI-related preferences (the “

”) and one that specifies how those preferences could be expressed in the widely used robots.txt files that many websites already publish (the “

”). In IETF parlance, these two drafts have been “adopted” by the AIPREF working group, which means the participants in the working group had rough consensus to pursue those approaches to meet the working group’s goals.

The policy token draft has no such consensus since it’s one individual’s submission that has not yet been considered by the group. After discussion, the group may or may not adopt it or integrate its contents into the attachment draft.

2. A training preference is nice to have, but a search-specific preference is missing, while the spotlight in the industry has shifted to inference.

Much of the discussion in the AIPREF working group over the last two years has centered on the definitions of new AI-related preferences in the vocabulary draft. These definitions matter because they are the categories for which sites will be able to express preferences in their robots.txt files (and via other technical mechanisms).

After significant discussion, the group has made progress towards settling on a definition of “AI training,” although discussions of some wording remain ongoing. After significantly 

 (and more heated) discussion, a definition of what it means for website content to be used in “search” has also been (mostly) provisionally agreed. Both definitions saw updates and new text added after the working group met in person at the end of August. Contentious discussions about a broader and more amorphous category — which has variously tried to cover “AI use,” “inference,” “grounding,” and related uses that occur separate from model training — are ongoing.

The policy token draft specifically leverages the “AI training” definition from the vocabulary draft and proposes a way that sites could express a preference to disallow all AI training, across all crawlers, by adding a few simple lines to their existing robots.txt files. The attachment draft already contains a similar mechanism, but the advantage of the approach in the policy token draft is that it would more immediately be machine-readable by many crawlers that are already deployed. This is the key value of the approach in the policy token draft: for site owners suffering from having to constantly update an ever-growing list of crawlers that they individually disallow, the newly proposed mechanism offers the prospect of a single, simple solution to express a preference to disallow all crawlers from AI training.

But what about search uses? And inference-time uses more broadly?

It is notable that the policy token draft does not provide an equivalent mechanism to cover search, especially given that the two companies that represent almost the entire web search market have signed up already to support it. It could have been quite powerful and clarifying for them to send the message that they want to make “search: yes; AI training: no” simple for sites to declare. The search definition in the vocabulary draft could have been leveraged in the same way that the AI training definition was. Publishers’ inability to express search-specific preferences has been the subject of regulatory investigation and intervention across multiple jurisdictions, making this omission all the more striking.

The new proposal’s focus on training also reinforces the difficult line-drawing that is still to come around inference-time uses more broadly. While the AIPREF working group has churned through multiple attempts at defining an inference or “AI usage” category, consensus has remained elusive, even as negotiation of licensing deals has 

 towards inference and recurring revenue. Microsoft’s announcement indicated that the company will combine support for the policy token mechanism with its own company-specific grounding preference mechanism, demonstrating the need for preference signals that go beyond training.

3. Since the mechanism has yet to be standardized, signatories to the EU AI Act Code of Practice are not required to commit to recognizing it.

Google’s announcement stated that the proposed mechanism is “consistent with the EU Code of Practice for General Purpose AI (GPAI) Model Providers under the EU AI Act.” Yet 

 is clear that it commits signatories to follow 

Signatories commit … to employ web-crawlers that read and follow instructions expressed in accordance with the Robot Exclusion Protocol (robots.txt), as specified in the Internet Engineering Task Force (IETF) Request for Comments No. 9309, and any subsequent version of this Protocol for which the IETF demonstrates that it is technically feasible and implementable by AI providers and content providers, including rightsholders

As noted above, with the policy token draft having been published less than a week ago, it has a ways to go before being formally standardized, extending RFC 9309, or demonstrating technical feasibility. As much as all involved surely want a speedy resolution, giving interested stakeholders a chance to review and debate the mechanism’s design is a critical part of the process that helps yield stronger standards that are more easily deployable and (hopefully) meet the needs of more website owners.

A common mechanism supported by the largest AI and search providers could substantially reduce the burden on publishers who must currently manage crawler rules one crawler at a time. But it is important to be clear about the limitations of what has been announced: the preference applies only to AI training, leaving out search and other inference-time uses. As an individual draft, it has many steps to go before formal standardization, during which time its design could change significantly. That process should be allowed to run before a few of the world’s largest companies decide the rules for the wider web to have to follow.

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Supporters of President Luiz Inacio Lula da Silva at an election rally on Oct 11, 2026 in Sao Paulo, Brazil. (Rodilei Morais/Fotoarena/Sipa USA)

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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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