David Linthicum

A college professor just exposed one of the biggest problems schools are facing in the age of artificial intelligence: students using AI to cheat without even reading what it gives them. In this story, Alcorn State University history professor Dr. Jason Gibson reportedly embedded hidden white text inside a midterm prompt. The students couldn't see it, but AI chatbots could. That hidden instruction told the AI to include bizarre references to Madagascar in the answer — and when dozens of students submitted responses mentioning Madagascar, the trap worked.

This wasn't a complicated AI detector, expensive software, or a high-tech investigation. It was a simple test of whether students were actually doing the work or blindly copying and pasting into ChatGPT. According to reports, 32 out of 35 students were caught, raising serious questions about academic honesty, how schools should adapt, and whether traditional assignments still make sense in the AI era.

In this video, we break down what happened, why the professor's trap worked, and what this story says about the future of education, cheating, and AI in the classroom.

Meta is facing a serious new lawsuit, and this one cuts straight into one of the biggest fears people have about AI at work: what happens when an algorithm helps decide who loses their job? According to recent reports, 26 current and former employees allege that Meta used AI-assisted or automated systems in a layoff process that unfairly impacted workers on medical leave, parental leave, pregnancy-related leave, and employees with disability accommodations.

The workers argue that if a company relies heavily on productivity data, ranking systems, or automated tagging, then people who were legally away from work or working under protected conditions can end up looking less productive on paper. That is the heart of this case.

Meta denies the allegations and says the claims lack merit, but the lawsuit is already raising a much bigger question: is AI making workplace decisions more objective, or is it just scaling bias faster than ever before?

In this video, we are breaking down what the employees are alleging, why the case matters far beyond Meta, and why this lawsuit could become a defining fight over AI, discrimination, and the future of layoffs.

MCP was supposed to be the breakthrough that made agentic AI actually work at scale. It promised a common language for tools, memory, and action — the missing layer that would turn language models into reliable digital operators. But in 2026, that promise is starting to collapse under real-world pressure. The deeper companies pushed MCP into production, the more they ran into the same problems: security risks, bloated context windows, fragile integrations, unpredictable behavior, and agents that looked impressive in demos but failed when the stakes became real. What was sold as the foundation for the next generation of AI is now being questioned as one of the biggest bottlenecks holding it back. And that matters, because agentic AI does not get judged on hype — it gets judged on whether it can perform safely, consistently, and at enterprise scale. In this video, we break down why MCP is increasingly being seen not as the protocol that unlocked agentic AI, but as the layer that exposed its weaknesses. If 2026 is the year the cracks became impossible to ignore, then 2027 may be the year the industry starts looking for something new. Because when the infrastructure fails, the future built on top of it fails too.

Forward deployed engineers are suddenly everywhere, and if you spend any time around enterprise AI, cloud, or data modernization, you've probably noticed the title showing up again and again. That is not an accident. The largest AI providers, the hyperscalers, and the big model companies have figured out that most enterprises are very good at launching pilots and very bad at getting those pilots into production. The technology works in a demo, but production is where things become expensive, political, messy, and slow. So the providers created a role designed to live inside customer accounts, remove friction, accelerate deployment, and make sure their platforms become the standard. That is why this matters. These people can be highly skilled engineers and very effective at implementation, but they are not neutral architects or objective advisors. They are funded, trained, and incentivized by the vendors whose technology they represent. Enterprises need to understand that distinction before they confuse implementation help with strategic guidance. In this video, I want to explain where this role came from, why it is expanding so quickly, what value it can provide, and why smart enterprises should engage it with open eyes and very clear boundaries from day one.

Ford just became the latest cautionary tale in the AI gold rush, and the lesson could not be clearer. The company reportedly leaned on AI-driven quality control systems, believing automation could take over work long handled by seasoned engineers. But instead of delivering the kind of precision and consistency the hype promised, the technology fell short where real-world judgment mattered most. The result was predictable: Ford ran into the wall that so many enterprises are about to hit, discovering too late that AI can automate certain tasks, but it cannot replace experience, intuition, and decades of institutional knowledge. Now, after pushing people out to make room for automation, Ford is bringing those experts back to provide the oversight the machines still need. That is not just a Ford story; it is an enterprise AI story. Too many executives are buying into inflated promises from vendors, cloud providers, and AI platform CEOs who keep selling transformation without properly explaining limitations. Companies absolutely own the bad decisions they make, but they are making them in a market drowning in hype. Ford is what happens when belief outruns understanding. And unless leaders reset expectations now, many more brands will repeat this same mistake.

Across the country, cities and towns are facing a difficult question: do AI-driven Flock cameras make communities safer, or do they push us closer to a surveillance society? In this video, David Linthicum takes a hard look at why so many municipalities are starting to push back. What began as a tool to help police recover stolen vehicles and identify suspects is now raising serious concerns about privacy, data retention, oversight, and the tracking of ordinary drivers who have done nothing wrong. As citizen outrage grows, local leaders are being forced to respond, in some cases by pausing deployments, rewriting policies, or reconsidering these systems altogether. But this is not a one-sided story. Supporters argue that these cameras can help law enforcement solve crimes faster, improve response times, and provide communities with a stronger sense of security. The real issue is whether cities can find the right balance between legitimate public safety benefits and the risks of constant monitoring. If you care about how AI is changing local government, civil liberties, and public trust, this is a conversation you need to hear. What happens next will influence policing, privacy rights, and public trust in technology for years to come.

Why are enterprises approaching AI so differently from cloud? In this video, we break down the major shift happening inside large companies as they move away from depending heavily on consulting firms and public cloud giants like Microsoft, Amazon, and Google. Instead of outsourcing strategy, architecture, and execution, many enterprises are building internal AI teams, creating their own roadmaps, and deploying homegrown solutions in data centers they control. We explore why AI is pushing companies toward ownership, customization, cost control, data security, and long-term competitive advantage. You'll see how concerns around vendor lock-in, rising inference costs, regulatory pressure, and the need for proprietary workflows are changing the enterprise technology playbook. We also look at why this moment feels different from the cloud era, when companies were much more willing to rely on outside partners. If you want to understand where enterprise AI is really going, and why self-reliance is becoming the winning strategy, this video gives you the key arguments, visuals, and talking points you need. Whether you work in tech, consulting, infrastructure, or the C-suite, this analysis will help you explain the trend clearly and see what it means for budgets, operating models, and enterprise power dynamics next ahead.

Enterprise AI is entering its most dangerous phase: the moment when spending outruns judgment. Companies are rushing into pilots, model deals, consulting engagements, and platform commitments before they have defined the business problem, cleaned up the data, or built the operational discipline required to make any of it work. That means the next chapter of AI will not be defined by breakthrough success stories, but by expensive failures, stalled deployments, weak governance, and public embarrassment. The real risk is not hesitation; it is reckless acceleration driven by executive panic and vendor pressure. In this video, David Linthicum breaks down where the AI wreckage is already forming, which enterprises are most exposed, and why so many organizations are about to waste millions chasing the wrong version of innovation.

In a world increasingly dominated by technology, some AI gadgets take the cake for sheer absurdity. This article dives into five of the silliest and downright stupid AI gadgets that are more entertaining than practical. From smart toasters that burn your bread with a personalized message to robotic pets that can hardly fetch, these inventions are a testament to the fact that not all tech is created equal.

Imagine an AI-powered mug that reminds you to drink water, only to annoy you with motivational quotes every five minutes. Or consider a voice-activated trash can that insists on giving you life advice while you dispose of your leftovers. These gadgets may not solve any pressing problems, but they certainly provide plenty of laughs and head-shaking moments.

Join us as we explore these quirky contraptions that highlight the humorous side of technological advancement. While they might not make your life easier, they will undoubtedly spark joy and conversation. After all, in a world of high-tech solutions, sometimes the silliest gadgets are the ones that remind us to not take life too seriously. Get ready for a lighthearted look at the wacky world of AI gadgets that are just plain fun!

Everyone is talking about autonomous AI agents like they're cheap digital employees, but the real story is far more complicated. In this video, I break down what actually happens when you let AI agents plan, loop, call tools, retry steps, update memory, and collaborate across workflows. On paper, the token costs can look surprisingly low. In practice, those costs multiply fast when you add multiple agents, long reasoning chains, browsing, orchestration, observability, security controls, human review, and enterprise integrations.

I walk through real examples across customer support, software engineering, research, and security operations to show how a few "helpful" agents can quietly turn into a meaningful budget line item. You'll see why the biggest expense often isn't the model itself, but the system wrapped around it.

This is not an anti-AI video. It's a reality check for founders, operators, technologists, and business leaders trying to understand when agentic AI is worth the money—and when simpler automation is the better choice. If you're building with AI in 2026, this is the cost conversation you need to have before scaling. By the end, you'll know how to think about agent cost per outcome, not just per prompt, model call, or demo alone.