The Flaws in Mass Layoffs for AI Productivity Are Beyond Obvious Now

Just when I thought I was done calling out tech CEOs for horrible mass layoff decisions, one of those CEOs doubles down on the mass layoff rhetoric. So here’s what we’re gonna do. You know how, when your mom or dad tried to give you solid life advice that just didn’t stick, they ended up sitting you down and listing out the flaws in your reasoning one by one? Well, sit down, kid. And like I tell all my kids: Look, I’m gonna yell at all of you, but I’m really only yelling at one of you. You’ll quickly figure out if you’re the one I’m actually yelling at, but it’s not gonna hurt any of you to hear what I have to say. This Could Be Any Corporate Tech CEO My opening histrionics aside, I want to make it clear that I’m not really trying to smack anyone individually. What I’m about to criticize is not the work of a single misguided leader, it’s the culmination of a spreading misguided follow-on leadership strategy. I also want to apologize if any of this comes off as flaming the writer or the publication behind the article I’m going to use as an example, because it literally could have been any tech CEO speaking to any publication after cutting double-digit percentages of their workforce and being all super-pumped about the future. It is such a bad look, so good job exposing it. But I guess we’re all numb to it now, when we read things like: “What [tech company’s] mass layoff tells us about the future of work” I encourage you to read the article. Go ahead and give the author some respectful clicks, because they get it right at the end, with facts. But ultimately you don’t have to read it because I’m about to take apart the “fire-all-the-humans-and-replace-them-with-AI” strategy point by point. This Isn’t a New or Novel Strategy Let’s start at the top: “Zeb Evans, CEO of the collaboration software startup ClickUp, claims that this shift is imminent. Last Thursday, Evans announced on X that the company, which was last valued in 2021 at $4 billion, had laid off 22% of its workforce.” A couple of things are quickly evident. First, this appears to be the same “everybody’s gonna eventually do it” reasoning that Jack Dorsey used when Block laid off 4,000 people just a couple months ago. Second, it’s not a coincidence that the company was last valued in 2021 at $4 billion or that that might have been its peak. 2021 was the acceleration point of the Great Labor Arms Race, and Corporate Tech companies across the industry started hiring people ill-equipped for poorly-defined roles at salaries that would have broken the bank if money wasn’t so cheap—or even free, via automatically forgiven loans. But the pretzel logic that gets used here is what makes this follow-on strategy especially obtuse. When Cutting Costs Isn’t Cost-Cutting The CEO “characterized that reduction as not a cost-cutting measure, but rather a radical embrace of AI that will propel the company to the next level.” Quick question: When is cutting 22 percent of your labor costs not a cost-cutting measure? I’ll just let that one hang. I’m not a mean person, really. The Biggest Mistake a CEO Can Make “‘Most savings from this change will flow directly back into the people who stay. We’ll be introducing million-dollar salary bands. If you create outsized impact using AI, you’ll be paid outside of traditional bands,’ Evans wrote.” One of the best lessons a mentor ever gave me about leadership is: The worst mistake a leader can make is looking at a chart that goes up and to the right and believing that chart will always go up and to the right. It never works out that way, but the temptation to think it will is always there. Using my mentor’s advice, I have several questions: Is this CEO talking about paying a percentage of profits based on whatever metrics they invent to show “outsized impact created by using AI”? Does the CEO plan to keep paying that employee their $1-million salary when the “outsized impact created using AI” returns to the mean? Quick follow up: Does that keep going until it breaks the budget or is this just, like, an MLM thing? If not, and the CEO does the sensible thing that every other company in the history of companies has called “commission,” won’t that employee just hop to the next company when that company offers them a $1-million salary to do what they just did? And then finally, let’s do the cut-throat AI thing that serves as the reason for the 22 percent “savings” in human labor: Once that “outsized impact created by using AI” materializes, why do you still need that employee? Especially if you’re now paying them a million-dollar salary? Isn’t that more “savings” just waiting to be “saved”? Making a Fortune Babysitting That last question kind of introduces another question: What are we paying these employees a $1-million salary for? “ClickUp recently introduced roughly 3,000 internal AI agents to handle a wide range of complex tasks on behalf of its employees…. Instead of performing the work themselves, staff members are now expected to direct these agents and ultimately review the output to ensure it meets the company’s standards.” Are we planning on paying million-dollar salaries to babysit agents? Because the last year has shown us that’s not where the seven-and-eight figure salaries are going. The 100x Productivity Myth “Evans’s goal, according to his X post, is for AI to turbocharge ClickUp into a ‘100x org.’” I’ve been on the AI front for over 16 years, and I get called a “100x guy” or a “10x guy” a lot. I don’t know what that means, but it sounds cool so I just smile and say thank you and get back to the data. Actually, I do know what it means, in another context, because I’ve spent my entire career as an entrepreneur and/or consultant, and have worked with a vast array of venture capital and private equity firms and their strategies. One of those strategies, familiar to everyone, is to create ROI by what I’m going to dub “numerator-maxxing” (see, I can make up buzzwords too). The strategy starts out logical enough. The company that the firm is investing in is doing something very right. Their numerator—the value that the company is generating—is a lot higher than their denominator—the money and sweat effort and brainpower being put into the company. The firm believes that the company’s numerator is artificially low and is being constrained by a weak denominator. So the firm dumps a bunch of money into the denominator. That’s their bet. When this happens, the numerator almost always increases. Where it goes wrong is when, a couple years into it, the numerator has not increased by orders of magnitude to compensate for being weighted down by a heavy denominator. One hundred over 10 is a much bigger number than 1,000 over 1,000,000. Sorry for the math. So yeah. Machines are less weight in the denominator than humans, and you can also add exponentially more of them to the denominator without it getting much heavier. But what do they add to the freaking numerator? Where AI and agent productivity is concerned, no one—no one—is looking at the numerator. Well, no, wait. Gartner took a look. Vindication Isn’t What It Used to Be Like I said, the writer gets a lot right at the end, and does it without my cartoonish fist shaking. In response to the metric most commonly used to measure that “outsized AI impact”: “[C]ritics argue that “tokenmaxxing”—as this concept is known—is the wrong metric because it simply racks up AI expenses.” In response to the company’s incredibly circular claim that people who automate their jobs with AI will always have a job: “But if AI keeps taking over more tasks, ClickUp will eventually need fewer and fewer people.” However, the most damning truth came from a quick mention of a quiet study from Gartner on ROI from AI-as-labor-replacement, just three weeks ago, from which I wish the writer had pull-quoted: “Many CEOs turn to layoffs to demonstrate quick AI returns; however, this disposition is misplaced,” said Helen Poitevin, Distinguished VP Analyst at Gartner. “Workforce reductions may create budget room, but they do not create return. Organizations that improve ROI are not those that eliminate the need for people, but those that amplify them by aggressively investing more in skills, roles and operating models that allow humans to guide and scale autonomous systems.” Am I still Don Quixote for screaming about this for the last 16 years? In fact, I said the same thing yesterday when highlighting the unintended consequences of this misguided follow-on leadership strategy, and it still feels like the loud part being said far too quietly. It will always be the humans behind the tech that will make the tech successful—not the babysitters, not gamification, not agentmaxxing, tokenmaxxing, or numerator-maxxing. So if any of those leaders see this rhetoric and still believe these mass layoffs are about AI and not about mistakes made by leadership a few years ago in hiring the wrong people for roles that were never clearly defined at salaries that never should have been offered, I’m begging you, think twice before you agentmaxx. The flaws are now obvious and documented. There is nowhere left to hide. EXPERT OPINION BY JOE PROCOPIO, FOUNDER, JOEPROCOPIO.COM @JPROCO

Anthropic suspends all access to Mythos model after US government bans foreign nationals use

AI company Anthropic has disabled customer access to its most capable systems after the US government ordered it to suspend all use by foreign nationals, Anthropic said in a statement Friday evening. The move is the latest in a series of adverse Trump administration actions targeting the company. The broad directive to Anthropic’s Mythos 5 and Fable 5 models is one of the furthest-reaching actions the government has taken in response to the advanced capabilities of an AI model. Anthropic said the US government gave it the directive, citing “national security” issues. The company said the government didn’t provide specific details about the national security concerns, though it believed the government had “become aware” of a method of “jailbreaking” Fable 5, or getting around its internal safety guardrails. “We reviewed a demonstration of this specific technique being used to identify a small number of previously known, minor vulnerabilities,” Anthropic said in its statement. “These vulnerabilities all appear relatively simple, and we have found that other publicly-available models are able to discover them as well without requiring a bypass.” Anthropic said it had instituted several safeguards for its newest models to “greatly reduce the likelihood” that they are “misused for tasks related to cybersecurity,” noting they’ve received complaints from users about those guardrails being too strict. Anthropic also noted it has worked with the US government to “red team” Fable’s safeguards and that no model is completely resistant to any jailbreak. Anthropic said that while they are complying with the directive and removing access to the models for everyone, “we disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people.” “If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers,” the company added. The restriction means that many foreign nationals working for Anthropic will not be able to touch those models. The Commerce Department, which issued the restriction, did not immediately respond to a request for comment. Axios reported the government’s directive would require Anthropic to obtain a license “for the export, re-export or domestic transfer of those Anthropic models.” Anthropic’s newest model, Mythos, has spooked the US government and Wall Street with its capabilities, which experts say can exploit cybersecurity vulnerabilities at an unprecedented pace. The model was seen as so capable, Anthropic initially limited its release to a group of key partners in order “to secure the world’s most critical software.” Anthropic released Fable 5 last week as a version of Mythos that is safe for general use. The model also helped spark the Trump administration’s recent executive order on AI, which asks companies to voluntarily share new models deemed to have advanced cyber capabilities with the government up to 30 days before providing access to other partners. One source with knowledge of early discussions of the executive order said the idea of banning foreign nationals from working on such models had been floated for that order, but the idea never made it into a draft. The government has had a complicated relationship with Anthropic. Earlier this year, the Trump administration blacklisted the company, declaring it a “supply chain risk” in military dealings over Anthropic’s insistence that the Pentagon include certain safety guardrails for the government’s use of AI in warfare. Anthropic sued the government over the designation as “unprecedented and unlawful” and notched at least one early win in the ongoing case. Despite President Donald Trump’s directive at the time of the designation for all of the federal government to cease working with Anthropic products, the White House has stayed in close touch with the company, and some parts of the federal government have found a workaround to continue accessing Anthropic’s models, especially after the release of Mythos. Anthropic was also deeply involved in helping draft the latest executive order, sources familiar with the situation told CNN, and its executives had been invited to the White House for a signing ceremony that was ultimately canceled at the last minute. By Hadas Gold

AI Is Wreaking Havoc at Starbucks and Pizza Hut. Social Media Is Having a Field Day

AI woes are coming for the food service industry, and social media can’t help but celebrate. This week, both Starbucks and Pizza Hut made headlines for negative news about their internal applications of artificial intelligence. At Starbucks, an inventory tool got the chop after making frequent counting mistakes, while at Pizza Hut, a delivery tool drove a franchisee to file a lawsuit. Social media users are saying the two stories may point to a larger trend—that for the first time in the AI era, more companies will pull away from AI than embrace it. Starbucks walks back an AI tool On Monday, Starbucks told employees it was retiring an inventory-counting tool powered by AI after the technology led to inaccurate counts and mislabeled products. “Starting today, Automated Counting will be retired,” said an internal company newsletter verified by Reuters. “Beverage components and milk will now be counted the same way you count other inventory categories in your coffeehouse.” In a statement to Fast Company, a spokesperson for Starbucks explained that the company’s choice to axe its Automated Counting tool is in line with its larger AI strategy, which is based on trial and error. “We test ideas in our coffeehouses, listen closely to partner feedback, and make changes to deliver a better, more consistent experience.” Starbucks’s move to ditch one AI tool doesn’t mean the company is forgoing the technology entirely. The company is still investing in internal AI applications, including an AI assistant for baristas called Green Dot Assist and an AI-powered order-sequencing system called Smart Queue. The brand is also experimenting with an integrated Starbucks app within ChatGPT. Pizza Hut’s delivery system backfires Where Starbucks’s choice to nix its AI tool came from the top down, the anti-AI sentiment at Pizza Hut started with a disgruntled franchisee. In a lawsuit filed on May 6, franchisee Chaac Pizza Northeast, which operates more than 100 Pizza Hut locations, alleged that the company forced it to adopt an AI tool called Dragontail, which inadvertently pushed average wait times from under 30 minutes to over 45 minutes in more than half of all orders. The complaint explained that the issue wasn’t with Dragontail itself, but with the information the tool provided to DoorDash drivers. Dragontail is meant to optimize food delivery by giving delivery drivers real-time updates on order preparations and timing. But according to the lawsuit, its implementation in 2024 caused “cascading operational breakdowns and customer dissatisfaction,” resulting in more than an estimated $100 million in lost business and enterprise value. Reportedly, once DoorDash drivers could see the real-time status of multiple orders through Dragontail, they would wait inside restaurants until multiple orders were ready, meaning some orders were being held for up to 15 minutes after they were ready for delivery. Because Chaac Pizza Northeast relies on DoorDash for all of its deliveries, the franchisee alleged that the forced change to its delivery model had a major impact on its sales. At its New York City locations, Chaac said its sales swung from positive 10.19% to negative 9.78% after implementing Dragontail. “With the intention to improve efficiency and service to the customer, Dragontail did the exact opposite,” the lawsuit stated. “It caused significant delays and pummeled consumer satisfaction.” Pizza Hut has not responded to Fast Company’s request for comment. Social media sees a trend With the stories from Starbucks and Pizza Hut breaking in quick succession, social media users are drawing connections between the two food service chains’ AI troubles. “Over the next 1-2 years we’re going to start hearing more reports about companies pulling back from AI than adopting AI, and markets aren’t ready,” one X user theorized. “The AI bubble might burst quicker than I thought,” echoed another. “You’re going to be hearing a lot more about forced AI integration and what a disaster it is for businesses and consumers,” a third user agreed. Other users pointed out that all of these problems could have been avoided if tasks hadn’t incorporated AI in the first place. “To err is human,” one person quipped, “but to really screw things up, you need a computer.” By Jude Cramer

Instagram’s AI Support Bot Made a Costly Mistake. It’s a Warning for Every Company

Sometime last weekend, hackers asked Meta’s AI support bot for access to someone else’s Instagram account. The bot said yes. The method, reported first by 404 Media, was almost insultingly straightforward: instruct the chatbot to add a new contact email to a victim’s account, confirm the change with a code sent to that email, then use it to reset the password. In some cases, all that was also needed was a VPN to spoof the victim’s location. Among the accounts taken over: the former White House page from Barack Obamaʼs presidency, cosmetic chain Sephora, and the chief master sergeant of the U.S. Space Force. While this hack was obviously a security flaw, most businesses should consider that this blunder also reveals a management error. The front door is now automated AI customer service is everywhere. According to MarketsandMarkets, the global AI customer service market was valued at $12.06 billion in 2024 and is projected to reach $47.82 billion by 2030, growing at a compound annual growth rate of around 25.8 percent. The economics are hard to argue with: AI scales cheaply, doesn’t require shift management, and doesn’t call in sick. AI has been shown to cut response times by 37 percent and reduce operational costs by 35 percent. Companies have moved quickly. Banks use it for dispute resolution, airlines use it for rebooking, telecoms route complaints through it, and e-commerce platforms use it to handle returns. Around 80 percent of companies are either currently using or planning to adopt AI-powered chatbots for customer service by 2025. Meta is not unusual for deploying AI support at scale. When Meta launched its AI support assistant in March, it was presented as a way to help users reset passwords, regain account access, and report problematic content. These are not trivial tasks. Account recovery involves identity, security, and trust—the kind of territory that, not long ago, companies deliberately kept behind a human review process because the consequences of getting it wrong were considered too serious to automate away. Meta has also reassigned thousands of employees into AI-related roles and used AI to automate risk assessments of updates, safety features, and content moderation—all of which are tasks that previously required human review. Therefore, the support bot exploit arrived in an organization that had been replacing human judgment with automated processes across multiple functions simultaneously. Authority without judgement The exploit worked not because the AI behaved unpredictably, but because the AI behaved more or less as it was designed. A user asked it to update a contact email, and it updated the contact email. The system executed a process it was authorized to execute, but in doing so, handed a hacker the keys. This distinction matters enormously for anyone responsible for deploying AI systems. Authority can be automated, but judgment is a different problem entirely. A junior employee in a similar situation might also make a bad call. Humans fail too, but organizations know how to supervise, train, audit, and escalate human decisions. Real people typically offer something an AI system can’t: the ability to notice that something feels off. In this case, hesitation at an unusual request, the instinct to double-check account history, and the recognition that a request arriving via VPN from an unfamiliar location (asking to change contact details on a high-profile account), warrants a pause that could have prevented disaster. Gartner forecasted that 20 to 30 percent of businesses will replace human customer service agents with AI by 2026. The efficiency case for doing so is well established, but what’s less well examined is the category of decisions that look like processes but aren’t. Account recovery looks like a process, but embedded within that process is a judgment call: Is this request legitimate? Humans often make that call poorly too. The difference is that when a human makes it poorly, the failure is legible. There’s accountability. There’s someone to fire. Victims of the Instagram account takeovers told 404 Media there was no way to escalate their problem and speak to an actual human. Historically, high-stakes support requests — account recovery, fraud disputes, identity verification — were escalated to human agents because the potential for harm was too high to leave it to a scripted response. The escalation path was a precaution for the edge cases that automated systems weren’t designed to handle. Research published in the Journal of Consumer Research found that customers evaluate service provided by bots less favorably than identical service provided by humans, partly because they perceive automation as the firm cutting costs at the customer’s expense. That perception hardens quickly when customers discover there’s no human available at all. The harder problem The lesson here isn’t that companies shouldn’t deploy AI in customer service, but the quality of the decisions being made around where AI gets authority and what happens when it exercises that authority incorrectly. The questions that matter aren’t technical; they’re structural. What decisions can AI make autonomously, and which ones require a human to sign off? What triggers an escalation, and who owns it? When the AI gets something wrong — not if, but when — can a customer reach someone who can fix it? Gartner concluded in mid-2025 that half of the organizations expecting to significantly reduce customer service headcount because of AI would abandon those plans by 2027, and that 95 percent of service leaders planned to retain human agents in a digital-first but not digital-only model. The companies that arrived at that conclusion before Meta’s incident are in a better position than the ones who’ll arrive at it after. The Instagram story will probably be remembered as an AI security vulnerability, and will likely get fixed as one. As of June 3, it reportedly still hadn’t been patched. As companies race to automate customer interactions in pursuit of lower costs and faster resolution times, they’re discovering that it’s far easier to reduce the cost of executing a decision than it is to replace the judgment behind it. With better training, retrieval-augmented verification, anomaly detection for suspicious requests, and well-designed human-in-the-loop escalation, AI systems may be able to take on increasingly sophisticated judgment-like tasks over time. But deciding which requests deserve more scrutiny than the process allows remains, stubbornly, a human problem for now. AI can execute authority at scale, but the question that the Instagram incident forces into the open is a simpler one: When it gets things wrong, who’s responsible? Right now, the answer at too many companies seems to be nobody. BY CONNOR JEWISS