American University, other top M.B.A. programs reorient courses around artificial intelligence; ‘It has eaten our world’
4 min
At the Wharton School this spring, Prof. Ethan Mollick assigned students the task of automating away part of their jobs.
Mollick tells his students at the University of Pennsylvania to expect to feel insecure about their own capabilities once they understand what artificial intelligence can do.
“You haven’t used AI until you’ve had an existential crisis,” he said. “You need three sleepless nights.”
Top business schools are pushing M.B.A. candidates and undergraduates to use artificial intelligence as a second brain. Students are eager for the instruction as employers increasingly hire talent with AI skills .
American University’s Kogod School of Business is putting an unusually high emphasis on AI, threading teaching on the technology through 20 new or adapted classes, from forensic accounting to marketing, which will roll out next school year. Professors this week started training on how to use and teach AI tools.
Understanding and using AI is now a foundational concept, much like learning to write or reason, said David Marchick, dean of Kogod.
“Every young person needs to know how to use AI in whatever they do,” he said of the decision to embed AI instruction into every part of the business school’s undergraduate core curriculum.
Marchick, who uses ChatGPT to prep presentations to alumni and professors, ordered a review of Kogod’s coursework in December after Brett Wilson, a venture capitalist with Swift Ventures, visited campus and told students that they wouldn’t lose jobs to AI, but rather to professionals who are more skilled in deploying it.
American’s new AI classwork will include text mining, predictive analytics and using ChatGPT to prepare for negotiations, whether navigating workplace conflict or advocating for a promotion. New courses include one on AI in human-resource management and a new business and entertainment class focused on AI, a core issue of last year’s Hollywood writers strike.
Officials and faculty at Columbia Business School and Duke University’s Fuqua School of Business say fluency in AI will be key to graduates’ success in the corporate world, allowing them to climb the ranks of management. Forty percent of prospective business-school students surveyed by the Graduate Management Admission Council said learning AI is essential to a graduate business degree—a jump from 29% in 2022.
Many of them are also anxious that their jobs could be replaced by generative AI. Much of entry-level work could be automated, the management-consulting group Oliver Wyman projected in a recent report. That means that future early-career jobs might require a more muscular skillset and more closely resemble first-level management roles .
Business-school professors are now encouraging students to use generative AI as a tool, akin to a calculator for doing math.
M.B.A.s should be using AI to generate ideas quickly and comprehensively, according to Sheena Iyengar, a Columbia Business School professor who wrote “Think Bigger,” a book on innovation. But it’s still up to people to make good decisions and ask the technology the right questions.
“You still have to direct it, otherwise it will give you crap,” she said. “You cannot eliminate human judgment.”
One exercise that Iyengar walks her students through is using AI to generate business idea pitches from the automated perspectives of Tom Brady, Martha Stewart and Barack Obama. The assignment illustrates how ideas can be reframed for different audiences and based on different points of view.
Blake Bergeron, a 27-year-old M.B.A. student at Columbia, used generative AI to brainstorm new business ideas for a project last fall. One it returned was a travel service that recommends destinations based on a person’s social networks, pulling data from their friends’ posts. Bergeron’s team asked the AI to pressure-test the idea, coming up with pros and cons, and for potential business models.
Bergeron said he noticed pitfalls as he experimented. When his team asked the generative AI tool for ways to market the travel service, it spit out a group of very similar ideas. From there, Bergeron said, the students had to coax the tool to get creative, asking for one out-of-the-box idea at a time.
Professors say that through this instruction, they hope students learn where AI is currently weak. Mathematics and citations are two areas where mistakes abound. At Kogod this week, executives who were training professors in AI stressed that adopters of the technology needed to do a human review and edit all AI-generated content, including analysis, before sharing the materials.
When Robert Bray, who teaches operations management at Northwestern’s Kellogg School of Management, realised that ChatGPT could answer nearly every question in the textbook he uses for his data analytics course, he updated the syllabus. Last year, he started to focus on teaching coding using large-language models, which are trained on vast amounts of data to generate text and code. Enrolment jumped to 55 from 21 M.B.A. students, he said.
Before, engineers had an edge against business graduates because of their technical expertise, but now M.B.A.s can use AI to compete in that zone, Bray said.
He encourages his students to offload as much work as possible to AI, treating it like “a really proficient intern.”
Ben Morton, one of Bray’s students, is bullish on AI but knows he needs to be able to work without it. He did some coding with ChatGPT for class and wondered: If ChatGPT were down for a week, could he still get work done?
Learning to code with the help of generative AI sped up his development.
“I know so much more about programming than I did six months ago,” said Morton, 27. “Everyone’s capabilities are exponentially increasing.”
Several professors said they can teach more material with AI’s assistance. One said that because AI could solve his lab assignments, he no longer needed much of the class time for those activities. With the extra hours he has students present to their peers on AI innovations. Campus is where students should think through how to use AI responsibly, said Bill Boulding , dean of Duke’s Fuqua School.
“How do we embrace it? That is the right way to approach this—we can’t stop this,” he said. “It has eaten our world. It will eat everyone else’s world.”
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AI doesn’t rebel—people design, deploy and profit from it. The real danger lies in allowing tech companies to escape accountability while shaping regulations that protect their dominance.
4 min
A wave of corporate warnings and technical disclosures has flooded the media, with headlines worrying over “swarms” of rogue artificial-intelligence agents launching “unprecedented” cyberattacks, outsmarting their makers, and inching toward a terrifying autonomy. The most revealing part of this narrative isn’t what the software did. It’s who is telling the story—and why. When corporate leaders publicly insist that the systems they financed, engineered and deployed are suddenly beyond their power to contain, skepticism isn’t only healthy; it is essential.
For years, Silicon Valley has drawn scrutiny from civil society and global regulators over tangible harms such as youth mental health deterioration and systematic privacy violations. Today, industry figures seem to be trying to change that public image. Loudly blowing the whistle on their own systems—just as two of the leading companies were preparing for massive initial public offerings—lets AI executives position themselves as a new generation of leaders who have come to terms with their societal responsibilities. They seem to want us to believe that they no longer want to “move fast and break things” but will instead stand as vigilant guardians between humanity and a technological apocalypse.
There is one glaring problem: Software doesn’t rebel. A mathematical model possesses neither intent, malice nor the will to defy its creators, let alone extinguish our species. AI is a human artifact, engineered for profit.
When an agentic model in an evaluation sandbox connects to an unauthorized server or executes an exploit, it hasn’t staged a coup. It has tried to meet the human-defined objectives set out before it through a path its designers failed to constrain. It’s the digital equivalent of the King Midas myth, in which the king’s ill-defined wish turns even his food and drink into gold.
That powerful experimental models were able to discover novel vulnerabilities and breach external systems isn’t a sign of a dangerous superintelligence but of human error or negligence. There is no sentient actor lurking in the weights to be reasoned with, feared or pacified. There are only human software engineers, product managers and corporate boards deciding which guardrails are worth the latency cost and which permissions can be skipped in the race to market.
Policymakers and voters need to resist AI exceptionalism. In any other discipline—from civil engineering to pharmaceuticals—courts and regulators treat a system failure as evidence of bad product design and inadequate safety testing. If an aircraft crashes, we focus on finding the engineering defect, correcting it, and enforcing established liability standards for the damage created.
By leaning on an anthropomorphic narrative, Silicon Valley attempts to repackage its specific human choices that led to experimental, powerful models behaving unexpectedly during tests as an existential peril. Elevating the issue to a cosmic scale leaves the public paralyzed and takes ordinary product accountability off the table.
In the cutthroat race for venture capital and market dominance, building guardrails slows down deployment. Grandstanding about uncontrollable power costs nothing and generates billions of dollars in free publicity, justifying stock prices, all while cultivating an aura of technological capability not only to build the frontier but also ultimately to rein it in.
Governments need to recognize regulatory capture when it stares them in the face. Tech leaders’ strategy looks transparent: Alarm Washington and Brussels into creating a regime in which only trillion-dollar incumbents with fully staffed compliance and safety departments can legally operate. By sitting at the policymakers’ tables before anyone else, these companies can help draft rules digging an impassable moat protecting them from open-source developers and upstart competitors, domestic or international. The real danger is in further concentrating the tech industry into the hands of only a few companies with deep pockets.
Beijing and Washington have brushed off those tech leaders’ calls, albeit for very different reasons. Chinese state media dismissed them as part of the “Cold War playbook” and intended to preserve U.S. dominance. Xi Jinping argued for exactly the opposite at the Brics Summit on Sept. 12, calling on Brics countries to “strengthen cooperation in the field of AI, encourage open source, openness, collaboration and sharing, and break new grounds and scale new heights.” President Trump, steeped in a doctrine of unfettered capitalism and technological supremacy, called fears that AI could destroy humanity a “hoax.” Vice President JD Vance warned that AI companies “begging the government to regulate them” looked like a “Trojan Horse.”
Striving to pursue its “European way” on AI and assert regulatory leadership, Europe, by contrast, welcomed the call. European Union President Ursula von der Leyen made this clear at the State of the EU speech last Wednesday and announced that the EU will invite “the main frontier labs for a discussion on how we can support ongoing industry efforts to pace the frontier.”
Europe has been here before. In an effort to lead global regulation and react to fears borne from ChatGPT, Europe rushed its landmark AI Act into law in 2024. Already the world’s most restrictive rulebook, the framework quickly proved too broad and complex to enforce. Stalled by implementation delays and concerns about European competitiveness, the EU postponed the law’s full rollout, leaving regulations uncertain.
AI should be regulated—risks exist and should be taken seriously. But governments need to act based on available evidence and verified facts, not corporate PR panic, the views of industry insiders, or the desire for quick political wins. The greatest danger facing society isn’t that software will awaken and overthrow its human masters. It is that we will allow the creators of the software to abdicate human responsibility for the systems they choose to build and help them pull up the ladder to market access behind them.
