David Linthicum

As artificial intelligence rapidly expands its reach, big consulting companies are confronting some of the toughest challenges in their history. This presentation examines how the democratization of AI—now accessible and deployable by firms large and small—has dramatically disrupted the traditional consulting value chain. Clients are leveraging AI tools to generate their own insights and solutions, often sidestepping the need for external consultants for standard analyses and operational improvements. As routine consulting tasks get automated or commoditized, pressure mounts on large firms to upskill, innovate, and redefine their unique value proposition.

The conversation will explore how consulting companies must pivot quickly: moving beyond "off-the-shelf" frameworks to offer advanced guidance on AI adoption, change management, and transformation at scale. Additionally, it considers the intensified competition from tech-focused boutiques and the heightened expectations of clients who now demand faster, more specialized results. Ultimately, the session emphasizes that for consulting giants to thrive in this new environment, they must reimagine their business models and embrace continuous learning, or risk being left behind by the AI revolution.

The surge of AI-generated content on YouTube—dubbed "AI slop"—is quickly becoming a major concern for both creators and viewers. With algorithmically produced videos flooding the platform, the human touch that once defined YouTube is being drowned out. Authenticity and creativity are sacrificed for quantity, leading to generic, repetitive uploads that make it increasingly difficult for thoughtful, original content to stand out. This overuse threatens not only discoverability, forcing high-quality voices into obscurity, but also the creative ecosystem itself, as genuine creators become discouraged and innovation stalls.

The consequences go deeper: as viewers encounter more formulaic, soulless videos, they begin to question the value and legitimacy of what they're watching. This erodes trust between creators and audiences, undermining community loyalty and engagement. YouTube's longstanding reputation as a source for creativity and connection is at risk. If this AI trend continues unchecked, the platform could become a wasteland of mass-produced, low-effort content, deterring aspiring creators and driving away viewers seeking meaningful entertainment. It's critical for creators to rethink their reliance on AI, refocus on what makes their work unique, and take responsibility for nurturing the vibrant, authentic community that built YouTube's success.

The debate over Artificial General Intelligence (AGI) is as heated as it is speculative. For years, the tech industry has oscillated between wild optimism and measured skepticism, with proponents promising radical transformations and doubters urging caution. AGI, by its most optimistic definitions, would be a machine capable of performing any intellectual task that a human being can, exhibiting common sense, creativity, and the ability to generalize knowledge across domains. Yet, as David Linthicum and other seasoned observers note, the very criteria for AGI remain frustratingly vague, and the path to its realization is far from clear.

Rather than marvel at singular, headline-grabbing breakthroughs in narrow AI, Linthicum's perspective asks us to interrogate what AGI really means and whether current technologies are even on the right trajectory. The conversation isn't just about new algorithms or faster chips; it's about fundamental questions at the intersection of computer science, philosophy, and society. Are we on the brink of machines that truly "think" like humans, or are we projecting our ambitions and anxieties onto tools that, however powerful, remain fundamentally limited? With hype and expectation running high, it's critical to approach AGI not as an inevitability, but as a profound challenge, still wrapped in uncertainty and debate.

The future of large language models (LLMs) is at a crossroads, threatened not by a lack of algorithmic progress, but by the shrinking pool of high-quality data. As website owners and content creators clamp down on web scraping—through technical blocks, legal restrictions, and opt-out movements—the vast text reservoirs that once fueled AI innovation are rapidly drying up. Paywalls, login barriers, and even "data poisoning" tools are making it nearly impossible for models to access the diverse, up-to-date information they need to advance. In this new landscape, LLM innovation isn't just slowing; it's facing a fundamental bottleneck. Without a dramatic change in data accessibility, the golden era of AI-driven language breakthroughs may soon come to an abrupt halt.

In a rapidly evolving work environment, a recent survey reveals striking statistics about the role of artificial intelligence (AI) in employee management. Almost 66% of U.S. managers are turning to AI tools, such as ChatGPT, to guide their decisions on layoffs, promotions, and raises. This reliance on AI raises significant concerns about the ethical implications of allowing machines to influence critical career outcomes.

The findings indicate a troubling trend where human managers become less engaged in decision-making processes, with nearly one in five admitting that they allow AI to make final decisions without any human intervention. While many managers acknowledge the importance of stepping in when they disagree with AI recommendations, the alarming truth is that over two-thirds of them lack formal training in AI, raising questions about the risks involved.

Are agentic AI solutions truly revolutionizing the enterprise, or are we just getting swept up by the hype? In this episode of "Dave Is Not AI," renowned AI expert Dave Linthicum—author, technologist, and top industry influencer—gives a brutally honest assessment of agentic AI. We'll unpack what agentic AI really is, why many "solutions" are just rebranded chatbots and RPA, and examine hard performance numbers from CMU and Salesforce revealing less-than-stellar success rates for real business tasks.

Discover why benchmarks like TheAgentCompany and CRMArena-Pro are essential for evaluating progress, and learn about the significant security and privacy concerns holding back enterprise adoption. Dave breaks down Gartner's predictions and sheds light on why most companies are stuck in "AI pilot hell," unable to scale these technologies.

Many enterprises are racing to implement AI, lured by promises of competitive advantage and rapid transformation. However, recent research shows that only about 10% of organizations investing heavily in AI achieve significant financial benefits. The greatest pitfall? Over-reliance on large consulting firms that force generic, cookie-cutter AI solutions into unique business environments. Surveys by MIT, Harvard Business Review, Gartner, and Forrester all reveal a pattern: enterprises guided by consultants often face stalled projects, disappointing results, and an inability to scale pilots into company-wide wins.

Why do these initiatives fail? Big consulting partners tend to prioritize reusable frameworks and fast "wins" over real, customized business value. They downplay the hard, messy foundational work—like data hygiene and change management—required for sustainable AI success. The result is wasted investment, superficial projects, loss of internal knowledge, and poor preparation for future innovation or regulation.

To truly achieve value from AI, companies must develop in-house expertise, focus on what's genuinely needed for their industry and culture, and build strong data and governance foundations. The lesson is clear: AI can transform, but shortcuts and generic strategies nearly always lead to disappointment, wasted resources, and missed opportunities for real innovation.

Duolingo, widely recognized for its engaging language-learning platform, has made headlines recently with its ambitious plan to evolve into an "AI-first" company. Founded by Luis von Ahn, the app employs gamification strategies to teach users various languages, making learning both accessible and enjoyable. However, von Ahn's announcement regarding the potential phasing out of contractors in favor of AI solutions has sparked a significant backlash, highlighting the growing anxiety surrounding the impact of artificial intelligence on employment. Critics argue that the transition to an AI-centric model undermines the human element essential to effective learning and raises fears of widespread job losses in the education sector.

Von Ahn's surprise at the negative reception of his vision underscores a broader disconnect that can occur when leaders fail to gauge market sentiments and employee concerns accurately. As the tech industry accelerates the adoption of AI technologies, it becomes increasingly crucial for CEOs to navigate these changes thoughtfully, ensuring that stakeholders feel secure and valued. This situation serves as a potent reminder of the importance of strategic communication and workforce integration as companies embrace technological advancements. Analyzing Duolingo's recent developments can provide valuable insights into how businesses can successfully transition in an era of rapid technological evolution.

Amazon CEO Andy Jassy recently announced significant changes to the company's workforce due to the rise of artificial intelligence (AI). In a blog post shared with employees, Jassy outlined that advancements in generative AI and automated agents would lead to a smaller human workforce as efficiency gains transform job functions. While he encourages employees to view AI as a collaborative partner, the shift raises concerns about job security and the potential for increased unemployment across various sectors. Jassy noted that while AI's impact could lead to fewer positions in some areas, new roles requiring different skill sets may emerge.

The implications of these changes are being felt beyond Amazon, with experts warning that AI could significantly affect the job market. Dario Amodei of Anthropic pointed out that AI technology might eliminate up to half of entry-level white-collar jobs, potentially driving unemployment rates as high as 20% in the near future. Critics of these predictions argue that there is insufficient evidence to support such claims. As companies evolve with AI, the conversation intensifies regarding how to prepare the workforce for an increasingly automated landscape while addressing the balance between innovation and job security.

Today, we're diving into a controversial move by Hertz, who has rolled out AI-driven vehicle inspections. The promise? Faster returns and fewer disputes. The reality? Well, a lot of frustrated customers, angry over surprise charges for so-called "damage" that barely shows up on camera. Is Hertz genuinely using AI to provide better service, or just to squeeze more fees from unsuspecting renters? Are these smart systems making the process more fair, or more alienating? In today's video, we'll break down the good, the bad, and the downright questionable behind Hertz's latest tech gamble. If you care about how AI is really impacting the customer experience—and not just what the press releases claim—you're in the right place. Ready to separate fact from fiction? Let's jump in.