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The Baruch Group, San Marcos, CA (2026)

06/01/2026

AI keeps inventing fake cases. Lawyers keep citing them

The trend of attorneys getting caught citing AI-hallucinated cases points to a broader problem: instead of checking AI’s work, people keep trusting it

BY STEVEN MELENDEZ EDITED BY ERIC SULLIVAN

Judges around the U.S. have sanctioned lawyers for filing court documents that include fake AI-generated citations. John Pendygraft/AFP via Getty Images
Artificial Intelligence
In April the Alabama Supreme Court sanctioned an attorney who had filed legal briefs laden with inaccurate citations generated by AI, including numerous references to cases that did not exist. After being informed he had cited a made-up precedent in one filing, the lawyer promised it wouldn’t happen again—but then cited “nonexistent cases at the end of the very next sentence,” as a justice noted in a concurring opinion. At least one other lawyer was sanctioned that week for continuing to file AI-hallucinated material after being warned not to do so.

A database maintained by Damien Charlotin, a senior research fellow at the Paris School of Advanced Business Studies (HEC Paris), lists more than 1,400 cases where courts have addressed AI errors in the past three years, including filings by attorneys and self-represented litigants. As recently as last fall, Charlotin says, the list appeared to be growing exponentially. It’s since leveled off to a steady flow of exasperated judicial rulings. “For the past two or three months, we have reached a plateau of around 350, 400 decisions a quarter,” says Charlotin, who has also created an AI-powered reference checker called Pelaikan.

Courtroom proceedings are public, and lawyers face sanctions for false claims, making such errors comparatively easy to track. But uncaught errors in AI-generated material have also ensnared journalists, software developers, academic researchers and government consultants, some of whom have been well aware of AI’s fallibility. On May 19 the New York Times reported that the author of The Future of Truth, a book about how AI is shaping discourse, acknowledged his text contained more than a half-dozen fabricated or misattributed quotes produced by the technology.

The pattern emerging across these cases is that people keep trusting AI’s answers even when they know the systems can be wrong. So far, that misplaced trust has led to dismissed legal appeals, attorney fines, fired journalists and software outages. Experts warn the stakes will rise as AI becomes more deeply embedded in professional work.

“Humans essentially have a tendency to believe that machines have more knowledge than they do, don’t break and are infallible,” says Alan Wagner, an associate professor of aerospace engineering at Pennsylvania State University.

AI also appears to inspire a particular kind of trust. It can generate answers that are realistic-sounding but false in a way humans seldom do—and people, it turns out, can find its guidance unusually believable. A study published this past February asked participants to complete an image classification task with guidance they were told came from either humans or AI. The guidance—no matter where it came from—was right only half the time, but among participants who were told the advice came from AI, those with positive attitudes toward the technology performed worse than those who held less favorable views. No such effect appeared when participants were told the advice came from humans.

The trend of attorneys getting caught citing AI-hallucinated cases points to a broader problem: instead of checking AI’s work, people keep trusting it

06/01/2026

Are You Meeting the Needs of the People You Lead?
by Mark van Vugt, Xiaotian Sheng and Wendy Andrews

May 13, 2026
LuckyBusiness/Getty Images
Summary. Organizations often assume leadership succeeds or fails because of a leader’s style. But research on follower psychology suggests the bigger issue is alignment: Employees judge leaders based on whether they provide what people need most in a given moment—protection, fairness, vision, expertise, affiliation, or status. Drawing on research across the United States, the United Kingdom, and China, the authors argue that the best leaders are not defined by a single leadership style, but by their ability to diagnose shifting follower needs and adapt before misalignment erodes trust, engagement, and performance.

Organizations have never invested more in leadership—and yet employees trust their leaders less than ever. Engagement scores among workers and managers are dropping worldwide, burnout is rising, and many leadership programs produce impressive frameworks but disappointing results.

The usual diagnosis is that leaders need to improve: Be more authentic, more empathetic, more transformational. But what if that diagnosis is wrong? What if leadership fails not because leaders lack the right style, but because they misunderstand what their people actually need from them?

Across industries, leaders are encouraged to develop a consistent style—to empower, inspire, and communicate purpose. These prescriptions are not misguided. But they assume something rarely questioned: That effective leadership looks roughly the same across situations.

In practice, it doesn’t.

A leader who empowers followers in moments of uncertainty may create anxiety rather than engagement. A leader who emphasizes empathy when followers long for clarity may make them momentarily feel better, but ultimately leave them directionless. A leader with a compelling vision who distributes rewards unfairly quickly loses trust. In each case, the leader may be doing what the textbooks recommend—yet still failing.

The problem is not effort or intention. It is misdiagnosis. Leadership is treated as a property of the leader, rather than as a relationship shaped by what followers need at a given moment. That shift in perspective changes everything.

The Hidden Psychology of Followership
Leadership does not reside in leaders alone—it exists in those that follow them.

People do not follow simply because of titles or authority. They follow because, consciously or not, they judge that doing so serves their interests. Every interaction with a leader is filtered through a set of implicit questions: Does this person make things better? Can I trust them? Do they help me succeed?

Across cultures, these evaluations are surprisingly consistent. Our research surveyed more than 3,500 working adults across diverse industries in the United States, the United Kingdom, and China. Using our validated Fundamental Follower Needs Inventory (FFNI), we confirmed that people rely on a set of six recurring criteria when judging leaders. They are the needs for protection, fairness, vision, expertise, affiliation, and status. These are not management trends or generational preferences. They reflect deeper expectations about what leaders are there to provide.

Our research helps explain a persistent puzzle: Why leaders who score highly on conventional competencies still fail to inspire trust or commitment. The issue is not capability. It is alignment.

A leader may communicate vision when people need protection. Or offer empowerment when people need guidance. Or demonstrate competence when people need fairness. In each case, the leader is not ineffective in general—but ineffective for the moment.

Organizations often assume leadership succeeds or fails because of a leader’s style. But research on follower psychology suggests the bigger issue is alignment: Employees judge leaders based on whether they provide what people need most in a given moment—protection, fairness, vision, expertise, ...

06/01/2026

Designing an end-to-end technology workforce for the AI-first era
April 6, 2026 | Article
By
Henning Soller
and Mike Stefanelli with Rajat Goel and Umar Husain

CIOs are re-building their teams for the agentic AI age. This requires rethinking how to hire, build internal capabilities, and negotiate vendor strategies to speed innovation.

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AI is forcing companies to redesign their technology organizations from the inside out. As agents take on more work, companies must rebalance how they approach tech hiring, internal capability building, and vendor negotiation. The end goal is to generate real ROI from AI, but getting the formula right is anything but simple.

For chief information officers (CIOs), it requires becoming architects of change in close collaboration with business leaders. Our research finds that two-thirds of top-performing companies have technology leaders “very involved” in crafting enterprise strategy, compared with 52 percent of other organizations.1 Together, CIOs and CEOs—working closely with chief human resource officers—can reshape their organizations to empower teams and generate maximum value.

Leaders can start by asking themselves three important questions: What new technology talent should we hire? Which capabilities should we help our employees build versus training agents to deploy them? Which type of vendor partnerships can deliver real ROI? This article will help CIOs chart a course forward, providing them with actionable tactics they can use to ensure their talent, skill development, and partnership strategies bring their companies into the agentic age.

Pressure from multiple fronts
Companies everywhere and in every sector must fundamentally rethink their technology organizations—and do so while facing four mounting pressures. Tech leaders are expected to cut overall costs while delivering more innovation. They must scale global delivery while managing increasing geopolitical risk. They are tasked with ensuring that partnerships with vendors deliver ROI, not just empty promises. And they must hire new talent while reskilling existing workforces—helping every employee become a technologist—while empowering human–agent teams to deliver AI at scale.

Let’s look at each of these four pressures in more detail to understand why CIOs are walking a tightrope today.

Cost pressures
In the past two years, the business environment has tightened. CIOs are facing increased pressure to reduce spending on “run” costs—the spending needed to keep tech infrastructure operational—to free up budget for innovative “change” initiatives such as AI rollouts. But run costs are rising, too, since AI requires robust infrastructure. That’s why almost every company is spending more on enterprise technology—and especially top-performing companies.2 A quarter of these companies plan to increase their technology budgets by more than 10 percent this year, compared with just 3 percent of other companies.3

Geopolitical risks
Geopolitical uncertainty further complicates the picture. Global capability centers have evolved from cost-focused delivery hubs into engines of innovation and product development. Yet this evolution brings new risks, including regulatory exposure, data security concerns, and decisions on where to hire. In this context, offshoring decisions that once seemed straightforward now require a more nuanced view of enterprise resilience and total cost of ownership.

Shifting vendor value chains
AI has forced a moment of reckoning between companies and their technology vendors. The software industry is entering a new shift where buyers and incumbent vendors alike must reimagine their value propositions, technology stacks, and operating models for an AI-centric era. For buyers, this includes integrating vendors and off-site teams more deeply into the company’s AI-native platforms, as well as scaling internal development to embed intelligence across workflows and products.

New workforce dynamics
The war for AI talent has never been fiercer, compelling companies to compete heavily for the most in-demand technical hires. On the flip side, agentic AI is doing more lower-level development work, engendering layoffs and requiring reskilling of remaining tech employees. High-impact employees are those who master gen AI as a superpower to deliver true transformation for their companies. As these competing factors play out, CIOs aren’t sure how to hire for today, let alone for the next three to five years.

Explore how CIOs are rebalancing hiring, capability building, and vendor strategies to drive ROI from agentic AI while navigating cost and geopolitical pressures.

05/31/2026

Companies Don’t Have to Slash Jobs Because of AI
Organizations that resist the push to trade entry-level workers for AI may end up better off in the long term.

Andrew Winston May 19, 2026
Harry Haysom/Ikon Images | Carolyn Geason-Beissel

SUMMARY: Many entry-level white-collar jobs are at risk in the near future, thanks to AI’s capacity to execute many of the tasks young workers have traditionally done. But companies that hold back from swapping artificial intelligence for real people may end up with an advantage: If they maintain the talent pipeline that has always served them, they could have a strategic advantage over their competition. There’s just one challenge: getting corporate leaders’ buy-in.
“IF AI IS going to destroy all the jobs, why don’t we just stop?”

That was the rhetorical question my college-age son asked after we talked about the possibility of drastic changes to career paths and society thanks to AI (technically, generative AI). It was in line with what I’ve been worrying about myself.

Nobody really knows how disruptive AI will be. But young people and their parents would be foolish not to prepare for deep, unprecedented change in how we work. A huge portion of entry-level white-collar jobs — the kind that college graduates normally flock to and count on as career springboards — may not exist in the near future.

I’m not alone in these estimations, obviously. Dario Amodei, the CEO of Anthropic, has been brutally honest about what he believes his products will do to hiring. He has (repeatedly) said that half of entry-level jobs — especially in fields like finance, consulting, law, and tech — are likely to disappear within a few years. Interestingly, he’s changed his tune very recently, suggesting that there’s an opportunity for job growth. But either way, the facts on the ground bear out the concerns. Reductions have begun: Goldman Sachs estimates that 16,000 jobs are evaporating every month.

So, what’s to be done? In a widely circulated clip from a May 2024 interview, former Google CEO Eric Schmidt put it plainly: Once AI agents develop a suite of skills that allow them to start working together on their own, away from our guidance, “we won’t understand what the models are doing.” His suggested solution? “Pull the plug.”

It’s a gut response that I feel a strong affinity toward, even as I dive deep into using AI myself. As I watch the world barrel toward a truly unknown future and the potential devastation that AI could wreak on job markets and young workers, I feel a mounting unease about how companies are starting to respond.

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What makes this challenge particularly hard to solve is that the executives making decisions about AI deployment and jobs will be fine regardless of how this plays out. They have capital, seniority, and options — financial and otherwise. It’s sadly uncommon for leaders to think beyond market cap and their own vesting schedules and consider whether we all can thrive. That inequality in exposure to risk is part of what makes this more than just a business question.

When society faces deep risk, companies and leaders tend to make choices that seem optimal for their short-term interests. From a pure short-term-profit perspective, bringing in fewer workers is probably the financially smart thing to do. But thinking about only the short term poses significant danger. With this latest existential challenge, if companies continue to head down a “people-light, AI-token-heavy” path, the risks aren’t just to young workers but to businesses, too.

The microeconomic case for some degree of caution is this: If companies decimate entry-level roles, what happens to the pipeline for leadership? Service businesses have long had a pyramid model where lots of young, smart kids come in and get trained and tested, and then a small subset make it to partner or other senior roles.

So, what if companies just didn’t eliminate as many jobs? Yes, we’re about 40 years into this model of businesses announcing cuts and their stock rising — investors often love companies that fire people. But what if, this time, they just didn’t? The companies that preserve human judgment, build institutional knowledge, and keep developing talent may find themselves with the advantage down the road.

Companies that defer entry-level roles to AI are losing their leadership pipelines. That’s risky and short-sighted.

05/30/2026

AI music is booming, and the player piano saw it coming

As AI songs get harder to tell apart from human-made music, an older technology offers a revealing preview of the fight over artistry, labor and pay

BY STEVEN MELENDEZ EDITED BY ERIC SULLIVAN

Inside an early 20th-century player piano. By translating punched holes on paper rolls into automated performances, the instrument acted as a predecessor to the digital code powering modern AI. Sepia Times/Universal Images Group via Getty Images
Artificial Intelligence
Recent research suggests listeners often struggle to distinguish music made by artificial intelligence from human-made songs—a sign that the technology has moved past novelty and into serious business.

In late February Suno, an AI music company based in Cambridge, Mass., announced it had reached $300 million in annual recurring revenue and two million paying subscribers, even as artists and record labels have continued to challenge how the technology was built and what it might replace.

Suno generates songs from written prompts, and it increasingly allows users to shape the results with lyrics, uploaded audio and voice samples. Paying subscribers get more control. Since last September Suno Studio, the company’s premium offering, has allowed users to manually edit its generated tracks. In March the company rolled out Voices, which lets subscribers generate songs using AI versions of their own voices.

Suno says more than 100 million people have accessed at least its free version. In a November 2025 post on the company’s blog, its CEO Mikey Shulman wrote that many were doing so “for the first time in their lives.” Existing musicians, from students to professionals, also use Suno to test ideas quickly, hear melodies in different styles and generate musical fragments for use in larger works.

“Our tools are designed to expand what people can create—to amplify the instinct, taste and feeling that only a person brings to music,” the company said in a statement.

For some musicians, the attraction is flexibility. Los Angeles musician and producer Yannick “Thurz” Koffi and collaborators recently used Suno to generate snippets in the styles of different eras and then used that material in place of the samples of existing songs often used in hip-hop. “We’re able to just use different elements from these generations and then throw them into our new compositions,” he says, “and make a bed for artists to jump in and create new ideas.”

That promise comes with a legal fight at the center of the industry. Artists and record labels say Suno was trained on copyrighted recordings without permission or compensation. In court, the company acknowledged that building its system required showing the model “tens of millions of recordings” but argued that such training is protected as fair use. https://buff.ly/WYLtGAv

As AI songs get harder to tell apart from human-made music, an older technology offers a revealing preview of the fight over artistry, labor and pay

05/30/2026

Six shifts to build the agentic organization of the future

By
Sandra Durth Julie Goran Alexis Krivkovich Ulf Schrader and Charlotte Seiler

When transforming to an agentic organization, the following six shifts stand out as critical to address today.

Workflows: End-to-end redesign, AI-first by design

Most organizations have treated AI as an add-on, layering copilots or chatbots on top of legacy processes. The result: modest productivity gains that rarely show up in the P&L.

To unlock step-changes in speed and productivity (i.e., value created per unit of effort), work should be reimagined as AI-first, domain by domain, beginning with desired outcomes. Humans should be brought in deliberately where they add unique value through judgment, empathy, and creativity.

Communication, collaboration, and knowledge management—all requiring holistic rewiring with technology—will then serve the additional purpose of eliminating silos and transaction costs, unlocking additional trapped value across domains.

Talent: A new human-agent frontier

Once workflows change, roles cannot stay the same. New capabilities will be needed—some that can be performed by technology and others that cannot—while some existing capabilities will no longer be necessary.

This will require rethinking of roles. Our research shows that 75 percent of current roles will need reshaping and new or different skill mixes, including more technological skills and greater emphasis on socio, emotional, and higher cognitive skills.

Structure: Dynamic organizations

Hierarchical pyramids built around knowledge silos will not survive the agentic era; scalable platforms will drive value. Organizations should take on leaner, flatter, and more fluid shapes, organized around autonomous “human + agent teams” that are steered toward outcomes.

Blended technology and human capabilities should become the new unit of value for a team, combining scalable productivity without human constraints and uniquely human abilities for accountability, oversight, and continuous progression. Teams should be dynamic, able to form and reform as priorities shift.

Leadership: Orchestrators of hybrid intelligence

Leadership roles should evolve as well. In the near term, leaders should be central to designing and delivering AI transformation despite uncertainty. Leaders will need stronger technological capabilities to enable the reimagination of domains with AI, system thinking, and the ability to make ethical decisions.

Leaders should support enterprise-wide change, communicating a clear narrative, assuaging fear, and driving adoption. They will be expected to become greater strategic visionaries—shifting focus to more strategic leadership, prioritizing outcomes over ownership. They should focus on driving cultural and ethical change by encouraging continuous experimentation, adaptability, and a “test and learn” mindset.

Culture and skills: Continuous reinvention as a competitive advantage

Organizations (and the humans within them) evolve linearly. However, technology is changing exponentially, and the gap between where organizations are and where they need to be continues to widen. Addressing this requires deliberate investment in continuous reinvention, bringing humans in the enterprise along and preventing organizational skill atrophy (i.e., when AI use hampers core skill development and practice).

Every employee should move beyond AI fluency—basic familiarity with tools—toward integrating AI in their daily roles. This will require reskilling and redeployment at scale, apprenticing new employees into co-intelligent workflows, and continuously upskilling current employees as agents take over foundational tasks.

At the same time, organizations should shift from an expertise culture to a learning culture. Starting at the top, leaders should wire continuous learning through the enterprise with new behaviors, mindsets, and incentives to build a dynamic system that can evolve as technology advances. Culture becomes the glue that ensures adoption sticks:

HR: Building a co-intelligent talent system

HR should become the engine of transformation, partnering with other business and technology leaders to reimagine jobs, redeploy talent, and embed trust into the system. Every HR product should be rethought, starting with strategic workforce planning to include both humans and agents, and decisions triggered should include both people and technology levers. HR should shape role redesign, reskilling at scale, hiring, apprenticeship and career journeys, performance management and incentives, as well as new organizational design.

AI—particularly agentic AI—is changing how organizations operate and how people create value. To capture the full promise of this technological wave, leaders will need to rewire workflows and reshape roles, skills, structures, and systems that hold the enterprise together.

05/30/2026

Putting frontier AI to work to strengthen defenses
By Jamie Thomas
Published 20 May 2026
As AI accelerates cyberattacks, the biggest risk for enterprises is no longer whether vulnerabilities will be found, but how quickly organizations can respond.

Frontier models are shrinking attacks that once took weeks into days or hours, while most security teams continue to operate through layers of manual reviews and approvals.

Closing that growing gap requires technology and ecosystem collaboration. That’s one reason IBM has been participating in Project Glasswing, an industry effort to protect critical software from AI-driven threats.

Through this work, we’ve been hardening our own products, contributing fixes back to open source, and sharing findings and best practices with other participants. This reflects a broader, sustained approach to building resilience against rapidly evolving AI-driven threats.

Building resilient AI defenses
As attackers increasingly use AI to speed up and scale cyberattacks, security teams are beginning to deploy frontier models of their own. One example is Claude’s Mythos Preview, a security-focused version of Claude designed to help identify and analyze software vulnerabilities.

IBM takes a multi-model approach to security—evaluating and deploying a mix of both traditional and AI-enabled tools based on their effectiveness, governance and risk controls. We’ve applied AI models, including frontier models such as Claude’s Mythos Preview to these capabilities across our defenses. For example:

Detection and analysis. IBM is introducing AI to assist in identifying and prioritizing critical threats. Our goal is to reduce the time between detection and remediation as attackers automate more of the threat chain.
Software lifecycle integration. IBM is working to embed AI-assisted capabilities across our software lifecycle to improve vulnerability assessment, software analysis, remediation prioritization and response coordination. A key focus is on leveraging AI code assistants, such as IBM Bob, to automate testing and test harness automation to enable faster remediation through accelerated testing.
Vulnerability remediation. We use both traditional industry-standard and AI tools to automatically triage and remediate vulnerabilities. The program incorporates newer AI tools, alongside more traditional software composition analysis, static and dynamic analysis, as well as scanners for images, secrets and other potential risks.
IBM’s approach still centers on core principles such as centralized governance, coordinated PSIRT processes, automated testing, engineering oversight and risk-based remediation. But we’re now weaving AI into each of those areas to keep pace with the ever-evolving threat landscape.

Actions companies can take today
As AI accelerates the pace and sophistication of cyberattacks, foundational security practices are even more critical.

Here’s how organizations can get started.

Reinforce zero trust architectures. Faster, more automated attacks reinforce the need for a zero-trust approach. At its core, zero trust recognizes that being protected does not mean being secure. Security cannot be assumed, which is why leading organizations operate with an “assumed breach” mindset. This approach assumes that identities, devices or networks may already be compromised and relies on continuous verification, using AI and analytics to validate access between users, data and applications in real time.
Keep software current. Maintaining up-to-date systems is essential as attackers accelerate the exploitation of known vulnerabilities. Organizations running outdated software or with aging infrastructure estates often struggle to deploy fixes or mitigations quickly enough to reduce exposure, creating a widening gap between vulnerability discovery and remediation.
Automate patch management. In an environment where vulnerabilities are identified faster than ever, the ability to assess, prioritize and deploy patches quickly is now a core security necessity. Organizations that treat patching as a continuous, risk-based process are better positioned to reduce exposure without disrupting operations.
Report vulnerabilities. Should a client identify a vulnerability themselves, they should not hesitate to report it to their vendors—IBM or others. For IBM, vulnerabilities can be cross referenced, and if necessary reported, as described on the IBM Trust Center.
We will continue sharing findings, fixes and best practices with the open-source community and the broader ecosystem to help strengthen collective defenses against rapidly evolving threats.

As AI accelerates cyberattacks, the biggest risk for enterprises is no longer whether vulnerabilities will be found, but how quickly organizations can respond. IBM’s multi-model approach to security aims to help companies with speedy response and remediation.

The Baruch Group

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