Artificial Intelligence

Artificial Intelligence – part 4: The Hype, the Dangers, and the Resistance

Technology • September 4, 2026 • Martin Hart-Landsberg

This is the fourth and final post in a multipart series aimed at cutting through the fog of AI hype in order to help us understand some of the dangers we face from AI use and highlight hopeful avenues of effective resistance. Part 1 provides an overview of my argument. Part 2 debunks the hype surrounding the multimodal generative AI systems produced by the leading AI companies. Part 3 critically examines the latest AI-powered technology, AI agents. This post highlights some current efforts to employ these AI tools in both private and public settings as well as possibilities for building a labour-community movement of resistance to the AI corporate offensive.

Multimodal AI systems and AI agents may not revolutionize society or business operations like their proponents predict, but that does not mean we have nothing to fear from their use. In fact, many companies and public agencies are already aggressively seeking to embed these AI systems into their operations, transforming work processes to the detriment of both workers and the quality of the goods and services they produce. Cutting through the AI hype was thus a necessary first step, making it possible for us to clarify the nature of the threat we face and to sharpen our thinking about how to build resistance and advance our own class interests.

What follows are only a few examples of the ways companies and public agencies are pursuing the use and further development of AI systems. In all cases their aim is to cheapen the cost of production by diminishing human agency with little regard for the well-being of those that use the goods and services they produce.

Healthcare is one of the sectors where corporations, in concert with AI developers, are moving fast to establish a critical role for AI agents. To this point, their focus has been on mental health, a rapidly growing market as illustrated by the popular use of general purpose chatbots for mental health support. One effort involves the development of the PatientGPT chatbot, the result of a collaboration between K Health and Hartford HealthCare. As Ars Technica describes:

“PatientGPT works in two modes: a generic medical question-and-answer mode that may incorporate information about the patient, or a “medical intake” mode, in which a patient starts providing symptom information and the chatbot gets less chatty and starts going through clinical flowcharts. After the AI agent collects enough information in intake mode, it will provide a next step, including setting up a follow-up appointment with primary care or seeking urgent or emergency care. If the latter is recommended, the chatbot stops responding to further questions.”

But no matter how sophisticated an agent may be, it still depends on a multimodal AI system to operate, and these systems, as previously discussed, have serious limitations that negatively affect their ability to record accurate information or provide appropriate recommendations for treatment. We can start with their ability to process information:

“A recent study found that human note takers create much better notes than AI-powered scribe tools. In some specific cases, the AI performed especially poorly compared to a human: when there was background noise; when the clinician and patient were wearing masks; and to a lesser extent when the patient had an accent, according to the American Medical Journal.”

And if the notes are not accurate then the directives or advice issued by AI agents that rely on them are also likely to be off the mark and potentially harmful for those depending on them. In fact, an audit of 20 leading AI scribe programs carried out by the Ontario auditory general concluded that these programs, which are designed to capture patient-doctor conversations, all:

“regularly generated incorrect, incomplete and hallucinated information that could ‘potentially result in inadequate or harmful treatment plans that may potentially impact patient health outcomes.’ That includes situations where an AI scribe hallucinated nonexistent referrals for blood tests or therapy, incorrectly transcribed the names of prescription medication, and/or missed ‘key details’ of mental health issues discussed in the simulated conversations.”

Undeterred, data collection efforts continue since the potential profits to be gained from employing an AI-powered mental healthcare delivery system are too great for companies to ignore. Thus, the Mayo Clinic is using what it calls an “Ambient Listening” program to capture all patient-nurse conversations in its facilities, including in its emergency rooms. It has also partnered with Abridge to create “an AI-powered clinical documentation platform, starting with approximately 2,000 clinicians who serve over 1 million patients annually across a wide range of specialties and care settings.”

Some companies are now actively working to build out agents to engage in actual therapy. For example, Talkspace is developing and training its model using its “140 million anonymized patient-provider messages, 6.2 million completed psychological assessments, 1.2 million therapist diagnoses and 4.3 million progress and psychotherapy notes.” Blueprint is another company using its own data to train its own therapy chatbot. Blueprint’s existing AI products currently create summaries of sessions to help therapists with billing and record keeping as well as real time recommendations for responses and treatments.

It doesn’t take long to imagine some of the many problems, for both workers and patients, that can be expected from employing AI agents as therapists. For patients, biases could well influence the system’s evaluation of their mental health concerns. And hallucinations could well lead to a misrepresentation or misdiagnosis of patient issues, potentially leading to life-threatening mistakes. For workers, it means cuts in the number of human therapists and greater challenges for those that remain who must help patients in crisis who may have been misdiagnosed or delayed needed treatment.

A number of government agencies, especially at the state level, aim to have AI agents manage their social safety net programs. Axios cites the work of a policy researcher who reports that:

“Some states are rolling out AI-assisted chatbots to answer Medicaid beneficiaries’ eligibility questions, Florida lawmakers included an AI system to check a user’s Supplemental Nutrition Assistance Program (SNAP) eligibility in its 2027 budget, and New Hampshire officials are working with Google Gemini to streamline how applicants submit information when starting an unemployment claim.”

As AI agents replace government workers, taking a more central role in gathering information and making decisions, many of those seeking needed services are likely to confront many of the same problems highlighted above. As a consequence, we can expect mistakes recording information and hallucinations when giving responses. People will not only be unfairly denied benefits but once mistakes become part of a person’s record, corrections will be difficult and time consuming to make.

News publishers are also pursuing the use of AI systems to write and interpret the news at the cost of working journalists and accountable reporting. For example, a number of McClatchy owned newspapers have begun employing what they call a “content scaling agent,” which “takes articles written by McClatchy staffers and rewrites and repackages them for different audiences and platforms” with no human oversight to make sure they are accurate.

Taking this one step further, the Associated Press’s Senior Product Manager for AI is pushing for a future where “reporters could go to events, get quotes, plug them into a large language model, and have the model generate a story, saving them time on writing stories they don’t feel passionately about.”

LexisNexis is already using AI agents to politically manage the work of its journalists. The company, which provides legal news and stories to more than 2.8 million daily subscribers, is now requiring the use of in-house created AI agents to ensure that everything written sounds “impartial.” Among other things, these tools must be used when drafting headlines and to identify and rewrite text that may indicate bias. A Nieman Lab story provides one example of the kind of sentences the LexisNexis tool finds problematic:

“On June 12, a federal judge ruled that the Trump administration’s decision to deploy the National Guard in Los Angeles in response to anti-ICE protests was illegal. Law360 reporters were on the breaking story, publishing a news article just hours after the ruling (which has since been appealed). Under Law360’s new mandate though, the story first had to pass through the bias indicator.

“Several sentences in the story were flagged as biased, including this one: ‘It’s the first time in 60 years that a president has mobilized a state’s National Guard without receiving a request to do so from the state’s governor.’ According to the bias indicator, this sentence is ‘framing the action as unprecedented in a way that might subtly critique the administration.’ It was best to give more context to ‘balance the tone’.”

Similarly, the Los Angeles Times has begun using its own AI system to label the political leaning of opinion stories and to produce AI-generated counterpoints to each story. “The feature came under fire for ‘bothsidesing’ morally unambiguous topics like the Ku Klux Klan.”

Education is another area where companies, especially AI developers, are eager to establish an important and profitable role for AI to the detriment of both educators and students. Encouraging student use of AI is far from risk free. One danger is that it can encourage AI dependence and there are already clear signs that prolonged engagement with large language model AI systems can cause great emotional harm, in extreme cases to suicide or murder. As an NPR story notes:

“there have been numerous reports of individuals experiencing delusions, or what’s being referred to as AI psychosis, after prolonged interactions with chatbots. This, as well as the concern over risks of suicide, has led psychologists to warn that AI chatbots pose serious risks to the mental health and safety of teens as well as vulnerable adults.

“‘We see that when people interact with [chatbots] over long periods of time, that things start to degrade, that the chatbots do things that they’re not intended to do,’ says psychologist Ursula Whiteside, CEO of a mental health nonprofit called Now Matters Now.”

Another danger from promoting AI use in schools is that it encourages students to accept AI systems as reliable and objective sources of information when they are not. Grok, for example, is heavily trained using posts on X, with priority given to those made by its owner, Elon Musk. Meta recently signed an agreement to draw heavily on the current reporting and archived content of the rightwing publication Newsmax for the training of its AI models. This is not a question of hallucinations but actual political bias that different companies are building into their respective systems.

Also of concern is the fact that numerous studies have found that significant use of these multimodal systems erodes the creativity and critical thinking skills of users, certainly not something to be encouraged with young learners. And yet the Portland Public School system recently encouraged classroom use of Lumi Story AI, a so-called AI literacy platform. As the Oregonian explains:

“Lumi Story AI is designed to help creators – in this case, middle and high school students – work with an AI-powered chatbot to write their own stories, comics and graphic novels. An image-generation tool pitches in for illustrations. Finished stories can be published on the Lumi platform; writers can order physical copies and even use Lumi Story’s AI tools to make and sell accompanying merch.”

All the major AI companies are spending billions of dollars to secure a dominant place for their AI systems. The Financial Times provides the following examples:

“Anthropic has just launched Claude for Teachers, a free tool aimed at K-12 (kindergarten to age 18) teachers in the US that aims to help them plan lessons. OpenAI has ChatGPT for Teachers – a free, self-serve offer for verified US K-12 teachers and staff. It also offers ChatGPT Edu, a discounted enterprise subscription aimed at universities. Google, meanwhile, has a suite of tools based around its Gemini models targeting teachers and students, building on its existing cloud tools for educational institutions.”

And like in healthcare, the race is on to gather more data to lay the groundwork for building ever more specialized AI systems, including ones that might actually replace teachers. For example, “University of Washington researchers planned to have preschool teachers wear cameras that would record everything they saw from a first-person perspective, including the children they were teaching, then use that footage to develop AI models.” In this case, parent outrage led to termination of the study.

Resistance to the AI offensive is clearly needed. And it will have to be a resistance with muscle developed through strong organizing and alliance building. The leading AI companies, still struggling with yearly losses, will not easily abandon any markets, especially ones with obvious growth potential. However, in a hopeful sign, unions have become increasingly alert to the dangers from this unchecked use of AI and have begun organizing accordingly.

Healthcare is a major target for those pushing AI. It is the sector that employs the most workers in the US, significantly more than either manufacturing and services. Moreover, the federal government funds a large share of healthcare spending, ensuring a relatively secure spending stream. That makes the growing resistance of organized healthcare workers to the AI-driven restructuring of health especially important.

The Oregon Health & Science University, the state’s only public academic health center, has been rapidly increasing its use of AI and without notifying or consulting with its unionized workers. Local 328 of the American Federation of State, County and Municipal Employees, which represents over 9,000 workers at OHSU, has begun surveying its members to determine all the ways in which AI is being integrated into the University’s operations and to track its error rate.

According to Angelo Bologna, the local’s chief Steward,

“the union is prepared to file an unfair labour practices complaint with the state Employee Relations Board over the failure to notify them of AI projects, through more work has to be done to document projects’ impact on workers.

“In addition to protecting workers from displacement or layoffs, the union wants OHSU to agree to having worker involvement in AI implementations, including multiple seats on the AI governance committee and involvement in the feedback process and error tracking. It also wants OHSU to provide notice to the union before any AI system is put into use, and to put limits on surveillance tools.”

In a blog post for union members, Bologna makes clear that AI use is absolutely changing working conditions and not for the better. He also points to OHSU’s learning health system to illustrate OHSU’s commitment to building out its AI program. As Bologna explains,

“They are training their LLM [large language model] on every patient interaction, every outcome, and every piece of work that you touch at OHSU.

“Here’s what we are fighting for:

Kaiser healthcare workers have also taken AI use very seriously, especially when it comes to mental health treatment. As the LA Times reports:

“Kaiser Permanente workers have been pushing back against the giant healthcare provider’s use of AI. They are building demands around the issue and others, using picket lines and hunger strikes to help persuade Kaiser to use the powerful technology responsibly.”

In 2025, Kaiser mental health workers held a hunger strike in Los Angeles to force the company to improve its mental health patient care. Bargaining is still ongoing in Northern California, and a key issue for the National Union of Healthcare Workers is Kaiser’s use of AI. In March 2026, some 2400 mental healthcare workers conducted a one-day strike in protest over “Kaiser’s efforts to replace human-provided mental health care with artificial intelligence.” As the political commentator and author Matt Stoller describes:

“They were protesting what they say is an illegal new triage system in their mental health and substance abuse help line. Instead of having well trained therapists making judgment calls about people calling in who might be suicidal, now those calls are handled by high school graduates reading off a script and using a checklist. [which is then fed to an AI system for decisions].”

Significantly, the strikers were joined by 23,000 registered nurses who shared their concerns about Kaiser’s increasing use of artificial intelligence.

The fight over AI use is also ongoing in a variety of media, especially print media. The Law360 Union, an affiliate of the News Guild of New York, is seeking to end LexisNexis’s mandate, as discussed above, that every story must pass through an AI-powered “bias” detection tool before it can be published. Nieman Lab shares the union response:

“Forcing journalists to use a tool on threat of discipline, with few formal guidelines and under constant surveillance, is not a recipe for innovation,” said Abraham Gross, a senior reporter and co-chair of the union’s AI subcommittee…

“The union’s AI subcommittee, including Gross, is actively negotiating adoption guidelines for the bias indicator with management, a process that is guaranteed by the union’s collective bargaining agreement.”

The Washington-Baltimore News Guild forced management at the political digital newspaper POLITICO to shut down the company’s “unilateral introduction of artificial intelligence tools that bypassed negotiated safeguards and undermined core journalistic standards.” That included the Capitol AI Report-Builder that generated policy reports for Pro subscribers and the “Live Summaries” feature which provided summaries of major political events. But were produced without any editorial review and were riddled with false and misleading statements.

A Communications Workers of America news story celebrated the victory:

“‘This is an extraordinary win not just for our members, but for everyone who believes journalism must remain in human hands,’ said Ariel Wittenberg, the unit chair. ‘We refused to back down, and POLITICO heard us loud and clear that these tools do not belong in our newsroom’.”

The situation is more complicated in education, where the American Federation of Teachers [AFT] has received some $23-million dollars from Microsoft, OpenAI, and Anthropic to establish a national AI training center for teachers the United Federation of Teachers’ headquarters in New York City. AFT President Randi Weingarten, according to a Yahoo Tech story, remains alert to the dangers of big tech capture but felt that the partnership was needed since “There is no one else who is helping us with this. That’s why we felt we needed to work with the largest corporations in the world,” Weingarten said. “We went to them – they didn’t come to us.”

This arrangement has been met with growing opposition from many teacher locals and state education unions, especially since the decision was made without any member discussion or debate. Trevor Griffey, a labour historian, lecturer at UCLA and UC Irvine, and vice president of legislation for AFT Local 1474 captures a large and growing sentiment among teachers when he told Truthout that:

“since the collaboration was established, the AFT national has focused too much on trying to help teachers adapt to AI, and too little on organizing worker power in school districts. He believes unions should go on the offensive and push for deeper reforms, as AI is already having a dramatic impact. He points out that groups like the California Federation of Labor Unions are trying to bring together statewide unions in California to lobby on a range of bills targeting the dangers of Big Tech.”

While important, struggles such as these over the use of multimodal AI systems remain largely defensive and siloed by sector and often by employer. As such they are unlikely to have the power to reverse corporate efforts to deepen our dependence on these systems. At the same time, there is no reason that they must remain limited in either their demands or vision. With creative and sustained organizing, they can become a powerful driver of the inclusive, class-based movement we need to defend our interests.

Here are three of many possible steps we can take to move the process forward. We need to create opportunities for unionized workers from different workplaces and industries to share experiences about how their employers seek to use AI systems and the efforts of their unions to win contract language protecting their rights. This kind of sharing can stimulate productive thinking about strategies for more effective organizing and bargaining. More importantly, it can also help workers see that the corporate embrace of multimodal systems and their associated agents has little to do with improving efficiency or product quality. Rather, it represents a new weapon in a generalized class offensive against working people, one whose primary aim is to undermine worker power and well-being.

Another step is to encourage union adoption, when appropriate, of a Bargaining for the Common Good negotiation strategy. Unions need to find ways to share their insights and common concerns, in this case over the use of AI-powered agents, with community members and win their support for a jointly fashioned set of demands that place restrictions on the use of those systems. Success will not only strengthen the hand of unions in bargaining, it also has the potential to encourage new visions of social organization as well as reinforce a class-based understanding of the forces undermining community well-being.

Finally, as the movement in opposition to this technology grows, those unions and community coalitions already in motion need to establish ties with the anti-data center movement. To this point these two movements have largely developed with little overlap even though there is a solid basis for common struggle. The fact is that the main driver of new data center construction is the continuing effort by AI developers to increase the power and speed of their systems. That is in part because:

“our current data centers cannot readily be retrofitted to become AI superhouses. The problem is as physical as the ground you’re standing on: Legacy data centers cannot bear the weight of the latest AI technology. The racks that house computer chips or AI chips are simply too damn heavy for the floors, and they would crack under the weight.”

Thus, there is a natural alliance waiting to be formed, one that brings together those opposing data center operations and their construction with those opposing the widespread use of multimodal AI systems in schools, workplaces, healthcare institutions, and government services. Such an alliance could serve as a strong foundation for building the kind of movement we need to assert popular control over the ongoing development and use of technology.

It is worth emphasizing that we should not think of this as an anti-technology or anti-AI movement. As the tech reporter and author Karen Hao explains:

“Before the industry made a hard pivot into developing extraordinarily resource-intensive AI models, a full breadth of other types of AI flourished: small, specialized systems for detecting cancer, for reviving disappearing languages, for forecasting extreme weather events, for accelerating drug discovery. So, too, did ideas to develop new AI technologies, including those that didn’t need much data at all, and those that required only mobile devices, not vast supercomputers, to train.

“Even now with large language models, an abundance of research and examples such as DeepSeek already show that different techniques can produce the same capabilities with a tiny fraction of the scale that AI companies use to justify their planet-consuming ambitions.”

In other words, as I previously stated in the conclusion to my overview of this series:

“AI as a broad technology is not our problem any more or less than are computers, the internet, or email. Rather, the problem is with the class interests shaping its development. More specifically, it is with the determination of tech leaders to develop a specific AI technology that undermines human agency, deepens our dependence on their desires, and threatens our economic well-being. The fact that we are witnessing an explosion of opposition to this form of AI by workers, consumers, parents, students, and community groups should give us reason for optimism. We need to unify and strengthen this opposition as well as expand its vision. It’s time for us to assert our own class interests.” •

This article first published on the Reports from the Economic Front website.

Martin Hart-Landsberg is Professor Emeritus of Economics at Lewis and Clark College, Portland, Oregon. His writings on globalization and the political economy of East Asia have been translated into Hindi, Japanese, Korean, Mandarin, Spanish, Turkish, and Norwegian. He is the chair of Portland Rising, a committee of Portland Jobs with Justice, and the chair of the Oregon chapter of the National Writers Union. He maintains a blog Reports from the Economic Front.