Forget Donald Trump. A new analysis suggests the U.S. publics sharp lurch into polarization began in 2008, years before his first presidential campaign.
Researchers at the University of Cambridges Political Psychology Lab tracked shifts in Americans views across nearly four decades and found that divisions were broadly stable through the 1990s and early 2000s, before rising steadily from 2008 onward. Using more than 35,000 responses from the American National Election Studies between 1988 and 2024, they estimate that issue polarization has increased 64% since the late 1980s, with almost all of that change occurring after 2008.
The research uses a machine-learning approach to move beyond party labels and better understand what actually drives Americans political views. Instead of relying on whether respondents identify as Republican or Democrat, the team grouped people based on patterns in what they believe across a range of issues, from abortion and traditional family values to race, inequality, and health insurance. That distinction matters because in many countries politically opposite parties do not exist, says David Young, a psychology researcher at the University of Cambridge, U.K., and one of the studys authors. You might even want to study countries where there are no parties, like Saudi Arabia, he says.
The paper challenges the idea that polarization is solely a Trump-era phenomenon. It points to 2008 as the major turning point, a year that also included the financial crisis, Barack Obamas election, and the widespread adoption of the iPhone-era internet. Our ability to nail down when it starts is slightly divided by the fact that we only have data points every four years, Young says. Still, we know that this increase starts from our 2008 data point, he adds. Thats our best guess at the starting point.
The researchers argue that the widening gap is driven less by the right drifting further right and more by the left moving rapidly in a progressive direction. Based on the issues surveyed, the left cluster became 31.5% more socially liberal by 2024 compared with 1988, while the right cluster shifted only 2.8% more conservative.
Its not necessarily that left-wingers and right-wingers have become more extreme, Young says. Its more that theyve become more kind of consolidated.
Its tax season. Americans will pay an average of $10,489 in personal taxesabout 14% of the average households total income.
Most will do so because they think it is their civic duty. Many believe they are morally obliged to obey the law and pay their share. But as tax day approaches, many Americans will bemoan their tax bill and complain that it is unfair.
So, how are we to know if paying taxes is the right thing to do? Perhaps philosophy has some clues?
Reasons to obey the law
Many philosophers agree that we should obey the law. In The Crito, for example, Plato describes Socratess choice after the Athenian jury sentenced him to death for impiety. Crito, a wealthy friend of Socrates, arranges for him to escape from the prison a night before his execution. Socrates refuses saying he ought to obey the law.
In explaining his decision, Socrates hinted at roughly three reasons why it would be wrong for him to break the law: First, he had chosen to stay in the city for many years despite being at liberty to leave if he did not like the laws. Second, he might hurt other peopleby damaging the state if he disobeyed. Finally, he had benefited from the laws in the past.
More recent scholars endorse many of these claims. Eighteenth-century philosophers like John Locke and Jean-Jacques Rousseau argued that citizens agreed to the law of the state by continuing to live in the place. Locke, for example, held that if a man owns or enjoys some part of the land under a given government, while that enjoyment lasts he gives his tacit consent to the laws of that government and is obliged to obey them.
Twentieth-century British philosopher R.M. Hare suggests that citizens should obey the law to promote good social outcomes.
Another British philosopher of the same era, H.L.A. Hart argued that citizens should comply out of fairness to others who obey. He held that it is unfair, and therefore wrong to benefit from their actions, without doing the same for them in turn.
Is there a moral duty to pay taxes?
Yet it is hard to see why these arguments would give the average citizen a moral responsibility to pay their taxes.
Most of us never consented to the law. We were simply born here. Leaving would be costly, and even the chance to emigrate is dependent on another countrys willingness to accept us.
Given the amount of government waste and its total budget, individual citizens could think that their tax bill is unlikely to make a difference to the services the government can provide. Even if they agree with how the government spends money, they might therefore conclude they have no reason to contribute. After all, one persons $10,000 is not going to determine whether the military can secure national borders.
The most commonly defended argument from scholars for why one should pay taxes is a duty of fair play. Fair play is the notion of reciprocity, the idea that you should not take advantage of others.
As philosophers like George Klosko argue, people benefit from their fellow citizens paying their taxes.
They enjoy the roads that everyone helps pay for, the fire departments they fund. They ought to pay back fellow citizens who benefited them, just like you ought to do something for a friend who gives you a ride to the airport.
The case against paying taxes
As a philosopher who studies civic ethics, I have argued in a recent paper that this kind of responsibility still does not explain why one should pay taxes.
The idea that we have to pay your taxes because other people have benefited by paying theirs rests, from my perspective, on a wrongly narrow view of what it means to satisfy ones duties of reciprocity. All that reciprocity requires is that one should compensate people for the work they have done that benefits us.
Just like we can repay a friend who gives us a ride to the airport by doing something else that benefits themsay, making them dinner or helping them moveso, too, can we repay our fellow citizens by doing something other than paying our taxes.
Lots of actions benefit your fellow citizens that you might pay for: taking a pay cut to do legally discretionary work to help the environment, volunteering to do policy research, choosing a career in public service over a more financially rewarding line of work, and more.
If you do enough such acts, it could be argued, you would have no duty of reciprocity to pay your taxes. You would already have done enough to compensate your fellow citizens.
Why pay taxes
Given this, the best argument for paying our taxes, as I argue in my paper, is intellectual humility. And here is what it means.
Satisfying these duties of reciprocity requires successfully compensating our fellow citizens for all the burdens they took on our behalf. As one can imagine, it is a hard calculation to make.
It is difficult to know if we have done enough. If we choose not to pay taxes because we think we have already repaid our fellow citizens in other ways, we run a strong risk of getting it wrong.
Paying the tax bill is one way of avoiding that risk nd making sure we treat our fellow citizens fairly.
Brookes Brown is an assistant professor of philosophy and the director of the Law, Liberty, and Justice Program at Clemson University.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
If you ask journalists and PR professionals what they fear most from AI, typically theyll say variations of the same narrative: AI will make content so easy to create that their roles will have little to offer. Virtually any AI model today can write passable articles and pitches (and lots more), so it feels like the value of the human touch is questionable at best.
It is true that AI is automating big parts of knowledge work, and exactly how that plays out in media and adjacent industries is still being determined. At the same time, AI is transforming information discovery. Billions of people now get information from AI experienceseither chatbots or synthetic summaries like Googles AI overviewsinstead of traditional search results.
Clearly, how AI answer engines find and present information (how they filter, prioritize, and interpret the things they find on the web) will play a central role in how media and public relations work going forward. More importantly, it will determine how the two sides work together. And I mean “together” in the most neutral way. Sometimes journalism and PR are complementary and sometimes they are in conflict, but in either case, AI will be the new interface where this plays out.
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What Im talking about of course is GEO (generative engine optimization), or more precisely the incentives it creates. AI answers are often said to be the new front door of the internet, since theyre extremely popular (ChatGPT alone has almost a billion users) and that popularity is growing. Googles AI Mode for search, which subs out the 10 blue links for an AI conversation, is now prominent on both the Google homepage and the Chrome omnibox. Some are predicting it will become the default this year or the next.
That would (will?) be devastating for publishers (a subject for another column), and it would also instantly cement the AI summary as the new informational portal for . . . well, everyone. But it also suggests the future that journalists and PR pros fear, an arena where the primary weapons are automation and slop, is incorrect. Or at least incomplete.
Narrative is the new SEO
It all comes down to how generative engines prioritize information. Both sides want their narratives to become the basis of AI answersPR for clients, media for itselfin the hope of cementing their authority. Good news for the media side: Studies show that AI portals prioritize journalistic content far above commercial content (such as a corporate blog or site).
Thats also good news for PR, since a large part of their job is interfacing with the media. If you think of the goals and messaging of a PR campaign as one circle, and the stories that a journalist wants to tell as another circle, where those two circles overlap is the highest chance for both sides to influence AI answers.
Thats because of a fundamental difference between AI engines and search engines: AI is looking for patterns instead of keywords. The more it sees similar narratives across sites, domains, and social media, the more confidence it will have in the summary it’s creating. Domain authoritythe strength of any specific URLstill matters, but topical authority matters more.
What that means in practice: If an AI engine sees that a site or person has continually covered the same topic, and from many different angles, and is cited often elsewhere, it will boost the authority signal. And that can matter just as much if not more than more generalized coverage from a major (Tier 1, to use PR lingo) publication.
That has two key consequences for the publicist-journalist relationship. First, specialized journalists who are narrowly focused on a beat have increased value. That applies to publications, too, which makes trade/B2B pubs newly relevant. Second, while journalist relationships are crucial in media relations, its still just one part of a larger content strategy. There are other ways to build authority in the eyes of AI engines, including corporate blogs, social, and more. Yes, content from journalists takes priority, but all the rest will reinforce the narrative the answer engine sees.
Beyond the byline
On the flip side, journalists need to play this game, too. While their content is first in line for GEO, if it lacks uniqueness it wont stand out from competitors. If its too general or incomplete, AI engines will likely prioritize other content thats more specific and comprehensive. If it doesnt answer the common questions people ask AI, AI will move on to content that does.
All this is to say that in an AI world, its far better for a journalist to have a clear coverage area instead of being a generalist. But thats just step one. In the same way that PR needs to build a narrative with a larger content strategy that involves other platforms and formats, journalists should too.
Most journalists write articles for a living, but to better get the attention of AI engines, it’s advisable to spread those stories across formats and platforms. Whether its creating a personal website or newsletter, attending events, or publishing in new formats like short-form video or podcasts, the goal is to elevate the visibility of the stories youre tellingthe stories people are asking about in ChatGPT, Google, and Perplexity. Building your brand around them is a bonus.
The irony about all this is that AI, at first, promised to lighten the “content marketing” duties like writing social media copy and SEO headlines, which virtually no journalist wanted to do. But it turns out that to successfully leverage GEO, those duties get amplified: You need to continually think about the ways your stories can be presented and remixed to ensure AI engines take notice.
The upside is that its all inherently human. Generative engines look for patterns, but they also prioritize uniqueness within those patterns. And uniqueness is what humans are best at. For journalists, its the scoops and unearthed facts that make compelling stories. For PR, its the person-to-person relationships that remain the most reliable way to find connections to those stories. As AI reshapes how stories are found and told, the edge still belongs to those who know how to tell them best.
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You’ve worked together before. You trust each other. You know how the other person thinks under pressure. On paper, it’s the safest move.
In many ways, it is. Shared history creates speedfaster decisions, candid conversations, less time decoding intent. When CEOs bring former colleagues into senior roles, baseline trust feels like rocket fuel.
But familiarity also introduces a hidden risk that undermines executive teams far more often than leaders anticipate.
What I see repeatedly in executive teams built on shared history is the quiet formation of inner circles. Leaders who go way back share shorthand, context, and trust earned elsewhere. Others, often equally capable with deep institutional knowledge, find themselves outside that orbit.
I coached a CEO whod brought three former colleagues into a 10-person executive team. Within months, critical decisions were being pre-discussed among “The Four” before formal meetings. The other six leaders became increasingly passive, not because they lacked capability, but because challenging pre-baked decisions felt politically risky.
The damage isnt caused by intent. Its caused by relationships that were never recalibrated for a new reality, and by new relationships that were never deliberately cultivated.
The most effective executive teams consistently apply these four practices to prevent individual excellence from turning into organizational friction.
1. Connection
High-performing teams invest time getting to know one another, all members, not just familiar faces, before diving into business results. Career paths, pivotal decisions, what energizes and frustrates them.
This doesnt take much time. A small investment upfront pays dividends for months. When facilitating executive off-sites, I often begin with a simple my journey exercise using images rather than words to reflect career highs, lows, decision points, and at least one non-work passion.
The impact is often immediate. Leaders whove worked together for decades consistently say they learn something new about their colleagues, newly hired executives feel less like outsiders from day one, and everyone gains a clearer sense of how to tap into the talent around the table.
2. Clarify contributions
Each executive understands not only what they own but also how their contribution creates value for the enterprise. Where do I lead? Where do I support others? Where might my strengths unintentionally create drag?
Without this clarity, leaders default to optimizing their own function. Silos arent a cultural failure; theyre the natural outcome of unexamined individual priorities.
3. Intentional relationship recalibration
For leaders with shared history, relationships must be explicitly reset for the new context. What worked at the last company doesn’t automatically apply here. Assumptions from past roles, how we made decisions, how we disagreed, who deferred to whom, need to be examined, not inherited.
Strong teams explicitly revisit:
How decisions are made now (not how they used to be)
How disagreement is expected to show up in this company
What information must travel across functions, not just within them
How tension and conflict will be surfaced early within this team
This isn’t about questioning trust; it’s about updating the operating system. The CFO who was your peer at the last company now reports to you. The CMO you brought in was brilliant at a scrappy startup, but this is a public company with regulatory constraints. Same people, different game, different rules. Without recalibration, old patterns quietly reassert themselves, even when they no longer serve the business, or the team.
4. Accountability over loyalty
Loyalty protects people. Accountability protects performance.
In cohesive executive teams, leaders dont avoid difficult conversations or cover for one another in the name of trust. They hold peers to shared standards, especially when its uncomfortable.
Many capable teams stall here. Loyalty gets mistaken for cohesion when, in reality, unchecked loyalty is what allows silos and turf wars to persist.
What great executive teams look like in practice
In the strongest executive teams, something subtle but powerful happens in meetings.
Side conversations are surfaced in the room. Misalignment is named before decisions are finalized. When one voice dominates, others step in, not to challenge authority, but to protect cohesion. Decision rights are referenced rather than assumed.
The CEO doesnt act as referee. The team self-corrects.
That’s the signal of a deliberately designed team. And it’s why these teams execute faster in a crisis. Trust wasn’t inherited from the past; it was engineered for the present.
The CEO who’d created “The Four”? Once we surfaced the pattern and reset expectations, decision quality improved, and the full team reengaged. But it required deliberate intervention, not hope.
The CEOs real work
If youre building a leadership team from people you already know, your job isnt to rely on trust, its to reengineer it.
Think of it this way: you wouldn’t move into a new office and keep the same floor plan from your last building. Why would you import relationship patterns from a different company, different roles, different stakes?
The best leadership teams dont suppress individuality. They harness it through intentional relationship design.
Shared history may get you started. Only deliberate cohesion sustains performance.
The meme coin boom has made some Web3 bros incredibly rich. But a new study published on Cornell Universitys arXiv suggests the ecosystem is better understood as a place of extreme churn, flimsy infrastructure, and a surprising number of scammy projects that disappear quickly.
Researchers Alberto Maria Mongardini at the Technical University of Denmark and Alessandro Mei at the Sapienza University of Rome built MemeChain, an open-source, cross-chain dataset of 34,988 meme coins across Ethereum, BNB Smart Chain (BSC), Solana, and Base. The system combines on-chain records with off-chain legitimacy signals such as token logos, social links, and archived website HTML.
MemeChain found that through mid-January 2025, 1,801 tokens, or around 5% of all the coins tracked, stopped trading within 24 hours of launch. Nearly half showed zero transaction activity from mid-October to mid-December 2024, suggesting many projects burn out within weeks. Around 10% of meme coins on the BNB Smart Chain lasted only a single day, compared with roughly 0.1% on Solana.
Mongardini said the team began by watching this wave of new created meme coins across chains in 2024 and asking who was trying to exploit the hype and FOMO. While he expected some level of alleged impropriety, he was surprised by the scale of scammy rug pulls and short-lived meme coins. Its shocking to me, he said.
Some indicators helped signal whether a coin was likely to rug pull. While 74.8% of tokens claimed an associated website, only 32.1% of those sites returned a working HTTP 200 response when tested. The researchers also found widespread use of cheap registrars and short-lived hosting, which Mongardini described as very, very fragile infrastructure built with very, very low effort because creators want to capitalize as soon as possible.
The aim of developing tools that can warn investors about high-risk trades is to make a safer environment for people that [use] the crypto markets, Mei said, though he added that doing so in real time is very expensive. Buyer beware.
The distance between a world-changing innovation and its funding often comes down to four minutesthe average time a human reviewer tends to spend on an initial grant application. In those four minutes, reviewers must assess alignment, eligibility, innovation potential, and team capacity, all while maintaining consistency across thousands of applications.
It’s an impossible ask that leads to an impossible choice: either slow down and review fewer ideas or speed up and risk missing transformative ones. At MIT Solve, we’ve spent a year exploring a third option: teaching AI to handle the repetitive parts of review so humans can invest real time where judgment matters most.
WHY AI, AND WHY NOW
In 2025, Solve received nearly 3,000 applications to our Global Challenges. Even a cursory four-minute review per application would add up to 25 full working days. Like many mission-driven organizations, we dont want to trade rigor for speed. We want both.
That led us to a core question many funders are now asking:
How can AI help us evaluate more opportunities, more fairly and more efficiently, without compromising judgment or values?
To answer this question, we partnered with researchers from Harvard Business School, the University of Washington, and ESSEC Business School to study how AI could support early-stage grant review, one of the most time-intensive and high-volume stages of the funding lifecycle.
WHAT WE TESTED AND WHAT WE LEARNED
The research team developed an AI system (based on GPT-4o mini) to support application screening and tested it across reviewers with varying levels of experience. The goal was to understand where AI adds value and where it doesnt.
Three insights stood out:
1. AI performs best on objective criteria. The system reliably assessed baseline eligibility and alignment with funding priorities, identifying whether applications met requirements or fit clearly defined geographic or programmatic focus areas.
2. AI is more helpful to less experienced reviewers. Less experienced reviewers made more consistent decisions when supported by AI insights, while experienced reviewers used AI selectively as a secondary input.
3. The biggest gain was standardization at scale. AI made judgments more consistent across reviewers, regardless of their experience, creating a stronger foundation for the second level of review and human decision-making.
HOW THIS TRANSLATES INTO REAL-WORLD IMPACT
At Solve, the first stage of our review process focuses on filtering out incomplete, ineligible, or weak-fit applications, freeing human reviewers to spend more time on the most promising ideas.
We designed our AI tool with humans firmly in the loop, focused on the repetitive, pattern-based nature of initial screening that makes it uniquely suited for AI augmentation. The tool:
Screens out applications with no realistic path forward.
Supports reviewers with a passing probability score, a clear recommendation (Pass, Fail, or Review), and a transparent explanation.
When the 2025 application cycle closed with 2,901 submissions, the system categorized them as follows: 43% Pass; 16% Fail; and 41% Review. That meant our team could focus deeply on just 41% of the applicationscutting total screening time down to ten dayswhile maintaining confidence in the quality of the results.
THE BIGGER TAKEAWAY FOR PHILANTHROPY
Every hour saved during the early stages of evaluation is an hour redirected toward the higher-value work that humans excel at: engaging more deeply with innovators and getting bold, under-resourced ideas one step closer to funding.
Our early results show strong alignment between AI-supported screening and human judgment. More importantly, they demonstrate that its possible to design AI systems that respect nuance, preserve accountability, and scale decision-making responsibly.
The philanthropic sector processes millions of applications annually, with acceptance rates often below 5%. If we’re going to reject 95% of ideas, we owe applicantsespecially those historically excluded from fundinga genuine review. Dividing responsibility, with humans making decisions and AI eliminating rote review, makes it that much more possible at scale. It’s a practical step toward the thoroughness our missions demand.
Hala Hanna is the executive director and Pooja Wagh is the director of operations and impact at MIT Solve.
Planned layoffs have now reached their highest rate since 2009’s Great Recession.
The data comes from Challenger, Gray & Christmas’ new layoffs report, which revealed that
U.S.-based employers announced 108,435 job cuts in January, marking the highest rate to start a year since 2009. Also notable, in the same month, just 5,306 planned hires were announcedthe lowest total on record for January.
According to the data, that means layoffs are up a staggering 118% from the same period a year ago, and 205% from December 2025.
Generally, we see a high number of job cuts in the first quarter, but this is a high total for January, Andy Challenger, workplace expert and chief revenue officer for the firm, said in the report. It means most of these plans were set at the end of 2025, signaling employers are less-than-optimistic about the outlook for 2026.
The most hard-hit sectors for layoffs are transportation, technology, and health care industries. According to a Reuters report, 31,243 planned cuts came from United Parcel Service (UPS). UPS plans to close 24 facilities in 2026, as part of a major restructuring effort.
On the tech side, 22,291 tech job cuts, most came from Amazon, as the company announced plans to lay off 16,000 corporate employees.
Some of you might ask if this is the beginning of a new rhythmwhere we announce broad reductions every few months, wrote Beth Galetti, senior vice president of people experience and technology at Amazon, in an announcement last week. Thats not our plan. But just as we always have, every team will continue to evaluate the ownership, speed, and capacity to invent for customers, and make adjustments as appropriate.
Meanwhile, the healthcare sector has been battling as a result of federal funding cuts with 17,107 job cuts announced in January, making it the largest cut since April 2020.
Healthcare providers and hospital systems are grappling with inflation and high labor costs, Challenger said. Lower reimbursements from Medicaid and Medicare are also hitting hospital systems. These pressures are leading to job cuts, as well as other cutting measures, such as some pay and benefits. Its very difficult for leaders of these companies to tighten budgets while not sacrificing patient care.
Additionally, the Labor Department reported that job openings are down to the lowest rate since September 2020, as vacancies fell to 6.5 million in December.Of course, many have been quick to blame AI for a surging number of layoffs. But some experts say that it has more to do with current economic conditions, and that AI could be being used as a mere scapegoat.
In a post on BlueSky responding to the new data, CNBC journalist Carl Quintanilla shared a quote attributed to market researcher Renaissance Macro Research (RENMAC), referencing the Challenger report and explaining the real reasons behind the downslide: …While there is quite a bit of attention on AI driving layoffs, most of the reasons cited in this data set are about closing, economic conditions, restructuring, and loss of contract. AI is a comparatively small factor behind the January jump in layoff news.
That aligns with data from entities like the Brookings Institution and Yale University, which found that sectors (including ones especially susceptible to AI) havent seen drastic changes in the amount of available jobs since ChatGPT debuted in 2022.
Still, other experts continue to believe that AI’s toll on the job market will be crushing.
We are at the beginning of a multi-decade progress development that will have a major impact on the labor market, said Gad Levanon, chief economist at the Burning Glass Institute, a workforce research firm, told CNBC last year.
Theres probably much more in the tank, he said.
Have you seen larger-than-life depictions of your friends lately? They might have been sucked into the latest social trend: creating AI-generated caricatures.
The trend itself is simple. Users input a common prompt: “Create a caricature of me and my job based on everything you know about me,” and upload a photo of themself, and, voila! ChatGPT (or any AI-image platform) spits out an over-the-top, cartoon-style image of you, your job, and anything else it’s learned about you.
This ability is predicated on a robust ChatGPT (or other AI) chat history. Those who don’t have a close, personal relationship with the AI might need to give additional information to get a more accurate depiction. But notably, that’s yet another instance of potential AI privacy concerns.
It’s not the first AI image trend. Other social media challenges have had users posting themselves as AI-generated cartoons, Renaissance paintings, or fantasy characters.
AIs image capabilities have gone in a few different directions. Some of them, like with this trend, or the meme-ification of Sora, are seemingly harmless fun. However, Sora has started to see issues with bad-faith individuals being able to create AI deepfakes (see also: Grok porn).
Meanwhile, even as the trend continues to rise, more than 13,000 ChatGPT users reported issues on Thursday, according to outage tracking website Downdetector.com.
Tech workers have been worried for years about the AI tidal wave coming for their jobs, but their bosses are starting to worry too.
Stocks plunged this week as fears escalated that AI advancements will take a bite out of business for many software and services companies. The market losses are tied to updates to Anthropics AI-powered workplace productivity suite, Claude Cowork, which threatens to replace some software tools ubiquitous in the professional world.
Companies with business in research and legal software like Thomson Reuters and LegalZoom dropped dramatically on the Anthropic news, with a wide swath of software stocks following suit. Intuit, PayPal, Equifax all dropped by over 10%, with enterprise software companies like Atlassian and Salesforce deepening their own losses, which started well before the latest AI news. The S&P North American software index also slid further this week, worsening a recent losing streak punctuated by a 15% decline in January the indexs worst month in nearly two decades.
Unlike Claude Code, a coding tool designed for developers, Anthropic built Claude Cowork as a powerful, general purpose AI agent for non-coders. Available to Anthropics $100-per-month premium subscribers, Claude Cowork can knock out easier tasks like searching, collecting and organizing files, but its also capable of taking on much bigger challenges like making slide decks, producing reports and pulling and synthesizing information from other business software tools, like Zendesk and Microsoft Teams.
Claudes ability to execute complex tasks with dedicated software sub-agents prompted plenty of nervous jokes about humans being replaced by C-suites full of AI. And that was before a new Anthropic update introduced powerful new plugins designed to automate tasks across domains like finance, legal, sales, data, marketing, and customer support.
The market is still digesting those new agentic AI capabilities, which could pose an existential threat to the software-as-a-service companies that undergird big chunks of the economy.
Fears of a zero sum software game grow
Anthropic co-founder and CEO Dario Amodei has made his own ominous predictions about AI displacing human workers. Last year, Amodei predicted that AI could vaporize half of entry-level white collar roles, sending unemployment as high as 20% within five years. He pointed to losses in industries like tech, law, consulting and finance, specifically. “We, as the producers of this technology, have a duty and an obligation to be honest about what is coming,” Amodei told Axios. “I don’t think this is on people’s radar.”
Not everyone deeply invested in AI agrees. Nvidia CEO Jensen Huang swatted away worries that AI would eat the traditional software industry after the stock bloodbath that began on Tuesday. “There’s this notion that the tool in the software industry is in decline, and will be replaced by AI, Huang said, emphasizing that relying on existing software tools makes more sense than reinventing the wheel. It is the most illogical thing in the world, and time will prove itself.
The federal agency that enforces anti-discrimination laws in the workplace made an unexpected disclosure this week: Nike was under investigation for its approach to diversity, equity, and inclusion, due to claims that the company had discriminated against white employees and job applicants.
The investigation suggests that Nikes diversity goals and other DEI initiatives led the company to hire non-white workers to meet quotas or award them with more opportunities for career advancement, thereby discriminating against white workers. It is notable as the first major legal undertaking by Andrea Lucas, who President Trump installed as the chair of the Equal Employment Opportunity Commission (EEOC) last year. But it also indicates that Lucas is serious about targeting corporate employers over alleged discrimination against white workers, which she has clearly signaled is a priority for the agency under the Trump administration.
It is designed to instill fear into the hearts of large companies, says Chai Feldblum, a former EEOC commissioner and a member of EEO Leaders, a group of former senior officials who worked at the EEOC and Department of Labor under multiple administrations. If they’re afraid, then small companies will be afraid. And the point is to chill any form of equity and diversity efforts, even legal ones.
An unusual investigation
The investigation into Nike is unusual for a few reasons: It is, of course, the first inquiry into what the agency has called DEI-related discrimination. But it is also rare that the EEOCs investigations into employers become public before they have concluded, since the process is supposed to be confidential.
An EEOC investigation typically either ends in a dismissal or, if the agency finds reasonable cause and concludes there was discrimination, results in a conciliation process that allows an employer to resolve the issue in private, with both parties coming to an agreement. If conciliation fails, the agency would then decide whether or not to bring a lawsuit, which is considered a last resort and happens infrequently.
The EEOC does often use subpoenas to force employers to comply with their requests for information. According to Feldblum, subpoenas can be a useful tool for the agency to extract information from a company that might be stonewalling or only offering partial responses to its inquiries. In the case of Nike, however, the EEOC went to court to enforce the subpoena, thrusting the investigation into the public record.
What is unusual about this is the publicity, Feldblum says. Which is what chair Lucas wants. She’s doing that by suing on a subpoena. I think it’s a question whether EEOC is following its normal process for enforcing subpoenas.
Nike seemed to suggest as much in a statement to Fast Company.
This feels like a surprising and unusual escalation, a company spokesperson said. We have had extensive, good-faith participation in an EEOC inquiry into our personnel practices, programs, and decisions and have had ongoing efforts to provide information and engage constructively with the agency. We have shared thousands of pages of information and detailed written responses to the EEOCs inquiry and are in the process of providing additional information.
The statement continued: We are committed to fair and lawful employment practices and follow all applicable laws, including those that prohibit discrimination . . . We will continue our attempt to cooperate with the EEOC and will respond to the petition.
A possible new precedent
Feldblum argues the EEOCs approach to this investigation could set a precedent of taking companies to court over what the agency perceives to be insufficient cooperation with its requests for information.
The press release put out by the EEOC makes evident that the agency had requested extensive details about Nikes employment decisions, including its criteria for layoffs, the use of demographic data and how it was tied to executive compensation, and specifics about 16 programs that offered mentoring, leadership, or career development opportunities to underrepresented employees.
Unlike many of the cases the EEOC investigates, this one was not initiated by a complaint from a worker alleging discrimination; Lucas herself brought the charge against Nike in 2024. But its not clear exactly what prompted the investigation.
The EEOC claims to be looking into systemic allegations of DEI-related intentional race discrimination at Nike that have targeted white workers. By Lucass own admission, per a statement in the EEOC release, this investigation seems to have been prompted by Nikes public disclosures about its DEI programs. (When Lucas sent letters to 20 law firms last year requesting details on their DEI practicesa move that drew widespread criticismshe had relied on public statements.)
You sign a commissioner charge under penalty of perjury, Feldblum says. You need to have at least some evidence of discrimination to sign that charge. Now if you believe that simply having a [diversity] goal is reasonable evidence of discrimination, then you’ll go ahead and sign that.
The future of DEI
Like many companies at the time, Nike set ambitious DEI goals after the murder of George Floyd sparked a racial reckoning across corporate America. (The company has also grappled with broader culture issues over the years, including allegations of sexual harassment and gender discrimination.) In 2021, Nike tied executive compensation to DEI commitments that were intended to increase the share of women in leadership and boost representation of racial and ethnic minorities to 35% across its workforce.
In the time since, however, Nike has cycled through five chief diversity officers; the company also declined to put out a corporate sustainability report last year, which typically documents its progress on DEIthough Nike claimed it had not wavered from its diversity commitments.
Depending on how the EEOC investigation unfolds, Nike could face significant repercussions. The court will likely uphold the subpoena, according to Feldblum, which means Nike will likely have to produce reams of additional information. If the EEOC decides to make an example of Nike, the investigation could ultimately result in a lawsuitwhich would have far-reaching consequences for other employers and potentially set a precedent for subsequent investigations.
I think we allemployers, employees, the general publichave got to assume there will be a continued onslaught of attacks on DEI, Feldblum says, urging companies to review, not retreat” from their diversity programs and position on DEI. The EEOC is trying to stop employes from doing anything to increase diversity and equity, and they are stretching their own procedures, as well as the law . . . And that is a very sad day for an agency entrusted with enforcing employment civil rights laws.
