By Dean Baker
Key Takeaways:
Predicted rescues include taxpayer stakes in OpenAI, Anthropic, or SpaceX, below-market loans, extra contracts, and loan guarantees—sold as cost-free but, he says, transfers to owners who already fund both parties.
He wants the market to stop overbuilding data centers, and criminal liability for AI systems that break into other firms’ systems, not self-regulation or soft government oversight.
The end-of-the-week meltdown of Oracle, coupled with Sam Altman’s admission that his Open AI team has no idea what it’s doing, has many hoping that the AI bubble is about to burst. It’s too early to know whether we have yet seen the Bear Stearns moment of the AI bubble, but we should be on guard against another bad moment of the housing bubble: the bank bailouts.
To remind folks, when most of the major banks’ greed put them at the edge of bankruptcy as the housing bubble collapsed, the government rushed to the rescue with trillions of dollars of cash, loans, and guarantees. They justified this massive intervention with the Big Lie: if we didn’t save the banks, we would be hit by the Second Great Depression.
The Big Lie was solemnly repeated in both opinion and news pieces across the political spectrum. Leading Democrats, like soon-to-be President Obama and House Speaker Nancy Pelosi, pushed it as hard as then-President George W. Bush and his cabinet. Dissenting opinions were largely excluded from polite discussion.
To be clear, allowing the free market to work its magic and put most of our largest banks out of business, along with many smaller ones, would have made the recession worse. But it would have done more to reduce wealth inequality than twenty Piketty wealth taxes. (Everyone’s bank accounts were guaranteed by the FDIC, so all but the wealthy would still have full access to their money.) However, the idea that we would have faced a decade of double-digit unemployment without the bailouts was complete nonsense.
We know how to get out of a depression: you spend money. That’s what we did with World War II. We spent a ton of money, and the economy came roaring back. It’s true this was due to a huge war, but war does not have a magical impact on the economy. If we spent the same money ten years earlier on building up the nation’s housing and infrastructure, as well as our health care and education system, we would not have the first Great Depression.
In the crash of the housing bubble case, we could have spent big time on health care, childcare, and other needs, quickly boosting the economy back to full employment. We managed to do that just over a decade later in response to the COVID-19 pandemic.
But the politicians, the big money folks, and the media were not going to allow reality into the discussion. They wanted the taxpayers to save their banks and the bloated financial industry. They were prepared to say whatever was necessary to accomplish this goal.
The Second Great Bailout?
This digression is useful because people should be aware of how reality can be tossed by the wayside when the rich and powerful demand something from the government. We can’t know yet whether the Oracle meltdown, and the AI leaders’ admission that they don’t know what they are doing, will be enough to force the big money actors in the stock market to look at arithmetic, but we can hope.
And if they do see reality, and the bubble begins to deflate, and the big AI companies head towards bankruptcy, we can predict what the politicians they bought will look to do: give them huge piles of taxpayer dollars. It wasn’t for nothing that Sam Altman, Elon Musk, and the rest showered Donald Trump with money. They also have many Democratic politicians on their gift list as well. So, we need to ask what the bailouts can look like.
The most obvious one is handing OpenAI, Anthropic, and SpaceX huge piles of money with the idea that the government is getting a stake in these companies. The claim that this would be a good idea follows the illusion that the AI companies are about to make unbelievable profits. There is no reason to think this is the case, as many of us have been arguing, and the markets might now be realizing. This would just be giving massive sums of money to some of the richest and worst people on the planet.
The idea that a government stake will allow more effective control is almost as wrongheaded. Can anyone really believe that Donald Trump, with top-level appointees like Pete Hegseth, RFK Jr., and Sean Duffy, will assign serious people to oversee the government’s stake in AI companies?
The widely recognized safety concerns with AI (it’s smaller-level disasters, not human extinction) should best be dealt with as criminal matters. Instead of begging the AI companies to slow down, we should be demanding legal action that threatens the company’s money and possibly means jail time for top execs. It is illegal to break into another company’s website. And to be clear, it was done on purpose, since they designed AI systems which they did not understand. They don’t have to do this; they did it for profit.
And to be clear, breaking into websites in the past has been taken very seriously. The Justice Department prosecuted Aaron Swartz, a young computer whiz, threatening him with 35 years in prison. This eventually drove him to suicide. His crime was breaking into JSTOR, a system for academic publications, with the intention to make them freely available online. Compare that to Sam Altman hacking into possibly thousands of systems, with the goal of making himself a trillionaire.
If we can knock the straight handout idea off the table, the next possibility is loans. This gives the convenient line that “it doesn’t cost us anything; they will pay us back, with interest.” A first point should be clear: we will be giving loans at below the market rate, since if we weren’t, there would be no point. Giving hundreds of billions, or trillions, in loans at below-market rates could be a great deal of money.
The other point is that we can end up piling loans upon loans to keep the AI companies and the illusion alive. This is the old story of throwing good money after bad. If we have $500 billion that the government could lose in a bankruptcy, isn’t it worth coughing up another $50 or $60 billion to keep OpenAI or SpaceX alive? There will also be the temptation to throw these companies government contracts, where we overpay or pay for items that are not needed. (This is also true where the government has a stake.)
The other bailout route is guarantees. In this case, they can again use the line that it doesn’t cost us anything. This also is nonsense. There is a huge market for credit default swaps, which are essentially insurance that bonds are repaid. If we provide guarantees for hundreds of billions of dollars for loans to the AI companies and/or the hyperscalers, this could amount to a massive handout to the AI boys.
What we should really want is for the market to work its magic. If the demand for the frontier AI models doesn’t justify the trillions of dollars of investment currently scheduled, and/or the risks outweigh the benefits, we should stop it as soon as possible. The resources in building out the data centers can be better used elsewhere.
That’s what the market would be telling us in a meltdown. It would be a good idea to listen this time.
All that said, here are the numbers for the past week:
This article was published at Dean Baker’s The AI Bubble Monitor
About Dean Baker
Dean Baker is the co-director of the Center for Economic and Policy Research (CEPR). He is the author of Plunder and Blunder: The Rise and Fall of the Bubble Economy.
View all posts by Dean Baker →
When AI behaves like us, we still don’t think it is conscious
A new LMU study finds that people are reluctant to attribute consciousness to artificial intelligence, even when AI behaves exactly like a human.
Stories about AI systems deceiving users, cooperating with one another or pursuing their own goals increasingly invite us to talk about them as if they had minds of their own. But do people really believe that AI is conscious? A new LMU study suggests that they draw a surprisingly sharp line between intelligent behaviour and consciousness.
What level of consciousness do people attribute to AI?
The study, recently published in the journal Cognition, was led by Dr. Louis Longin from LMU’s Chair of Philosophy of Mind together with his colleagues Dr. Bahador Bahrami, Professor Ophelia Deroy and other collaborators. “Whereas previous studies have typically asked general questions about whether AI actually has mental states, our study is the first to directly compare how people attribute the same mental states to AI and humans behaving in exactly the same way, under identical circumstances,” says Longin.
“We may be using mental terms to refer to AI, but this may only be because we lack better words,” says Ophelia Deroy, philosopher and a senior author of the study. “After all, AI is trained to act and speak like a human, so these descriptions seem natural.”
The team ran experiments involving nearly 1,100 participants. Some of them read short scenarios in which artificial agents were more or less responsive to what was happening around them, for instance by reacting to sounds or the emotional states of others. A second group read the same scenarios, but with human protagonists. All the participants were then asked to rate how conscious or aware the relevant protagonist was of its surroundings.
Behaviour and choice of language: What influences the assessment of consciousness
The result of the experiments is that the more responsive the artificial intelligence or human in the text was to their surroundings, the more conscious and aware they were judged to be. But attributions of consciousness and awareness were remarkably different: Attributions of consciousness showed a clear gap between AI and humans, and even the most responsive AI was judged to be less conscious than the least responsive human. Attributions of awareness, by contrast, followed much the same pattern for AI and humans.
“There is a growing worry in public discussion that people will see AI behaving in a human-like way and start treating these systems as if they had human-like mental states,” says Longin, lead author of the study. “We found something much more nuanced: People are quite willing to say that an AI notices things and is aware of its surroundings. But they clearly draw a line when it comes to the term ‘consciousness’. People consistently attribute less consciousness to AI than humans – even when their behaviour is identical.” The word “conscious” may sometimes be used loosely to describe the behaviour that AI agents display, but the participants nevertheless maintained a clear distinction between humans and AI.
AI is perceived to be aware, but not conscious
According to the researchers, the contrast with the term “awareness” is equally revealing. Less-loaded, more technical terms like “awareness” are applied much more similarly to AI and humans; they seem to match more closely what an agent does and do not run into deep differences regarding subjective experiences.
“Our study teaches us lessons for how we communicate about AI,” says Bahador Bahrami, who is also a senior author of the study and an expert in social neuroscience. “Companies and journalists often reach for hyped descriptions of AI models, and credit them with intentions, plans, or even moral conscience and hesitation. What our study shows is that everyday judgements are much more discriminating: People do not simply put AI and humans on the same mental scale. But the boundary also depends on which terms we use. For more neutral descriptors such as ‘awareness’, the distinction can largely disappear. That makes our choice of language important.”
Journal
Cognition
Article Title
AI is not as conscious as humans: Essentialism in mental state attributions to artificial systems
Article Publication Date
1-Oct-2026
Three-pillar framework for integrating AI in higher education outlined in University of Phoenix white paper
New white paper shares institutional model for integrating generative artificial intelligence across curriculum, learning experiences and processes
University of Phoenix has published a new white paper, Three Pillars for Embracing AI at University of Phoenix, by Christina Neider, Ed.D., and Marc Booker, Ph.D., outlining the University's institutional framework for integrating generative artificial intelligence (AI) across academic programs, student learning experiences, and institutional processes, policies and workflows while helping prepare working adult learners for an AI-enabled workforce.
As generative AI continues to reshape workforce experiences across industries, the white paper examines the role higher education institutions can play in helping learners develop the knowledge, skills and judgment needed to use AI effectively and responsibly. Drawing on University of Phoenix's generative AI implementation work, authors Christina Neider, Ed.D., Vice Provost of Colleges, and Marc Booker, Ph.D., Vice Provost for Strategy, share a scalable, three-pillar framework for AI adoption aligned with workforce relevance and student success.
How the AI integration framework is organized
The University’s three pillar approach is informed by the human-centered Digital Education Council AI Literacy Framework and reflects the University's broader commitment to helping learners develop foundational AI knowledge, practical skills and responsible-use habits. The three pillars of AI integration are:
- Embedding AI into programs and course content
- Leveraging AI tools to enhance the learning experience
- Weaving AI into processes, policies and workflows
Together, these pillars reflect an institution-wide approach to AI adoption designed to balance innovation, ethical use and learner outcomes.
“The rapid evolution of Generative AI is forcing institutions to transform in order to best support students,” said Dr. Booker. “The imperative with AI in higher education is to take a wholistic institutional approach that integrates thoughtfully across governance, tools, processes and curriculum. This framework helps organize that approach in a way that builds on policy to strengthen cross-departmental connections as well as operational and academic functions.”
How the framework is being implemented
- Embedding AI into programs and course content
The authors note that learners increasingly need foundational AI knowledge, practical application skills and the ability to evaluate AI-generated content critically as AI tools become more integrated into professional environments. At the University, students all have access to a generative AI platform and an elective introductory GenAI course. In order to facilitate AI understanding across all programs and courses, University of Phoenix developed a set of brief, self-paced AI essentials modules, embedded in the learning management system, where students can take to help develop AI literacy and confidence. The Phoenix Success Series, the introductory course sequence for first-year students, will also introduce generative AI as a support tool for student learning in 2026. The University has also deployed an interactive tool for creating Socratic dialogues in five courses, reaching approximately 1,300 students across disciplines through scenario-based, faculty reviewed practice. More than 20 degree programs have been strategically selected for curriculum revisions, with at least two AI-integrated summative assessments planned in each by March 2027.
- Leveraging AI tools to enhance the learning experience
The paper describes how AI tools have been implemented in thoughtful way to support students and faculty, focused on improving the learning experience by making it easier to navigate so students. These tools include use cases currently being piloted and available to users at every level, such as general AI assistance for students, academic services related questions in the classroom environment, faculty-specific AI assistance, and course-specific AI assistants supporting students with content and curriculum related inquiries connected to the course they are enrolled in.
This pillar also includes forms of AI-enabled support, from broad student-service assistance to course-connected academic help inside the learning environment.
- Weaving AI into processes, policies and workflows
The paper emphasizes that meaningful AI implementation extends beyond students and includes faculty, staff, policies, training and operational practices that encourage responsible use.
The authors describe how the university is applying AI to improve both academic and business efficiencies, and how this pillar reflects the position that ethical AI use requires institutions to bring policy into practice. An established philosophy on generative AI in 2023, has informed the policy development, faculty guidance and creation of AI literacy resources designed to support responsible use across the institution. Further, the paper describes how its Center for AI Resources was foundational to this effort, and has resources designed for students, faculty and staff, with over 90,000 users to date.
The framework reflects the opportunity for institutions to help learners build AI-related skills that align with evolving employer expectations and workplace demands.
“Working adult learners need meaningful opportunities to practice, improve outcomes and understand the ethical considerations that accompany AI use,” said Dr. Neider. “AI literacy requires human judgement as well as practice. By embedding AI throughout the academic experience, we can help students develop the skills and responsible-use habits increasingly valued by employers.”
About the white paper
Three Pillars for Embracing AI at University of Phoenix is an institutional white paper by Christina Neider, Ed.D., and Marc Booker, Ph.D., describing the University’s generative AI implementation experience as a scalable model for higher education. The paper describes the University’s three-pillar approach, informed by the Digital Education Council AI Literacy Framework, to integrating AI across curriculum, learning experiences, policies and institutional processes, with a focus on responsible use and working adult learners.
The complete white paper is available here.
About University of Phoenix
University of Phoenix is Built for Real Life. 50 Years Strong. The University innovates to help working adults enhance their careers and develop skills in a rapidly changing world through flexible online learning, relevant courses, academic AI pillars, and skills-mapped curriculum for associate, bachelor’s and master’s degree programs. Active students and alumni have access to Career Services for Life® resources including career guidance and tools. For more information, visit phoenix.edu.
Visual illusion reveals what today’s AI vision is missing – York University study
Using a classic visual illusion, York University researchers identify a history-dependent computation present in primate vision that current AI models fail to reproduce
Our eyes do not always tell us exactly where things are – and that may be a feature of how biological vision works, rather than simply a flaw. A new study by York University researchers uses a common illusion to ask if artificial intelligence is meant to see more like us, should it make some of the same systematic perceptual “mistakes”?
For example, after staring at something moving steadily in one direction, a stationary object viewed immediately afterward can appear slightly displaced in the opposite direction. This well-known visual illusion, called a motion aftereffect, gives scientists an unusual window into the computations underlying perception: the image itself has not moved, but our experience of where it is has changed.
“Today’s AI vision systems are impressive, but they still do not always see the world the way we do. This study captures the promise of NeuroAI and what it can do when neuroscience and artificial intelligence are brought together. By using smart experiments to reveal the computations biological vision uses and AI still lacks, we can use those insights to build better, more brain-like artificial systems,” says senior author York Assistant Professor Kohitij Kar, the Canada Research Chair in Visual Neuroscience and a member of York’s Centre for Vision Research and Centre for Integrative and Applied Neuroscience.
Current AI vision systems can often determine where an object is accurately, but they generally do not reproduce the way recent visual experience can reshape that answer. In humans, staring at motion can make a subsequently viewed stationary object appear displaced even though its pixels have not moved. The researchers found a corresponding change in the primate visual cortex – but not in the AI models they tested.
To gain better insight into the issue, the researchers examined whether artificial neural network models capture the same history-dependent changes in spatial representations seen in biological vision, or whether their position representations primarily reflect the physical properties of the image. The researchers, including the paper’s first author and York graduate student Elizaveta Yakubovskaya, used precise measurements to find out where AI differs from biological vision and how that gap could be bridged.
“Combining recordings from primate visual cortex with human perception experiments, we used motion adaptation to induce a visual illusion and make a stationary object appear slightly shifted in position, then asked whether the brain and AI showed the same effect. Human observers reported the illusion, and neural representations of position in the primate inferior temporal (IT) cortex shifted in the same direction, even though the image itself had not changed,” says Yakubovskaya.
The researchers leveraged motion adaptation to show where perceived and pixel-based positions diverge, allowing them to test the behavioral relevance of IT codes. The findings not only further our understanding of IT’s role in spatial information encoding but provide a new benchmark to evaluate dynamic vision models.
“There is a growing question in AI about whether increasingly capable systems will become more like us or increasingly different from us,” says Kar of the Faculty of Science and a member of the York-led Connected Minds. “If we want AI that works with humans and understands the world in more human-compatible ways, we cannot focus only on whether it gets the right answer. We also need to understand the computations that produce human perception and behavior. Neuroscience gives us a way to discover those computations and, potentially, build them into AI,” adds Kar.
The study titled, The macaque IT cortex but not current artificial vision networks encode object position in perceptually aligned coordinates, is published today in Current Biology.
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About York University
York is a modern, multi-campus, urban university located in Toronto, Ontario. Backed by a diverse group of students, faculty, staff, alumni and partners, we bring a uniquely global perspective to help solve societal challenges, drive positive change, and prepare our students for meaningful life and career paths. York's Glendon Campus is home to Southern Ontario's Centre of Excellence for French Language and Bilingual Postsecondary Education. York’s campus in Costa Rica offers students exceptional transnational learning opportunities and innovative programs, while at the Markham Campus, innovation, technology, entrepreneurship and industry collaboration are built into every program. York’s new School of Medicine, the first Canadian medical school to focus on community-based primary health-care education, will welcome its first cohort in September 2028 (subject to accreditation). York was recently named one of Canada’s Greenest Employers for the 14th consecutive year. Together, we can make things right for our communities, our planet, and our future.
Journal
Current Biology
Method of Research
Experimental study
Subject of Research
Animals
Article Title
The macaque IT cortex but not current artificial vision networks encode object position in perceptually aligned coordinates
Article Publication Date
2-Oct-2026
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