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The largest federal workers union says ‘untrained, armed’ ICE agents should not replace TSA

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America’s largest federal employee union says ICE agents are unqualified to replace TSA officers at US airports.

“ICE agents are not trained or certified in aviation security. TSA officers spend months learning to detect explosives, weapons, and threats specifically designed to evade detection at checkpoints — skills that require specialized instruction, hands-on practice, and ongoing recertification,” Everett Kelley, president of American Federation of Government Employees, said on Sunday in a statement posted online.

“You cannot improvise that. Putting untrained personnel at security checkpoints does not fill a gap. It creates one,” he added.

The statement came one day after President Donald Trump said he would tap ICE agents to help with airport security as the partial government shutdown drags on.

“Likewise, I look forward to moving ICE in on Monday, and have already told them to,”GET READY.” NO MORE WAITING, NO MORE GAMES!” Trump wrote on Truth Social.

White House Border Czar Tom Homan said Sunday that the administration was actively working on a plan to integrate ICE agents into airports.

“We’ll have a plan by the end of today on what airports we’re starting with and where we’re sending them,” Homan said on CNN’s “State of the Union.”

The partial government shutdown has left the Department of Homeland Security, which oversees the TSA, unfunded as Congress debates its immigration enforcement policies.

TSA officers haven’t received a paycheck in five weeks, and more than 400 have quit since mid-February, according to The White House, compounding a staffing shortage. As a result, long wait times and massive lines are clogging airport security checkpoints.

On Sunday, Kelley said that many TSA agents have continued to show up to work despite the lack of pay. “They deserve to be paid, not replaced by untrained, armed agents who have shown how dangerous they can be,” he said.

ICE has been at the center of Trump’s immigration crackdown. Fatal shootings sparked widespread protests against the agency earlier this year and contributed to the removal of former DHS Secretary Kristi Noem.

Kelley called on Congress to “stop playing politics and do their jobs.”

During an interview on Sunday, US Transportation Secretary Sean Duffy said TSA officers, whose salaries start around $40,000 annually, can’t live on $0 paychecks.

“They’re going to take other jobs to put food on the table and pay the rent,” Duffy said on “This Week with George Stephanopoulos.” “I do think it’s going to get much worse, and as it gets worse, I think that puts pressure on Congress to come to a resolution.”

Disruptions to air travel were what ultimately pushed Congress to end the previous full government shutdown.




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AI agents failed at real-world consulting tasks — but Mercor’s CEO says they’re still on track to replace consultants

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New research suggests an AI agent can’t fully replace a human consultant — at least for now.

Mercor, the AI training giant, tested how well leading AI models, acting as agents, performed real-world consulting, banking, and legal tasks.

The models failed most of the time, but Mercor’s CEO, Brendan Foody, told Business Insider that the results tell only part of the story.

The consulting tasks in Mercor’s APEX-Agents benchmark were designed to simulate real management consulting work, based on expert surveys and input from consultants at McKinsey, BCG, Deloitte, Accenture, and EY.

Across all task categories, the AI agents successfully completed the tasks less than 25% of the time on the first try. Given eight attempts, the agents could only complete 40% of the tasks. For the management consulting tasks, OpenAI’s GPT 5.2 initially performed the best, completing nearly 23% of the tasks on its first attempt. Anthropic’s Opus 4.6, released this week, performed even better at nearly 33%.

While many of the tasks were not completed, Foody said the success rate for GPT 3 was only 3%, compared to 23% for GPT 5.2. Anthropic’s model went from 13% to 33% on consulting tasks in a matter of months. Foody said he expects the success rate of the models to be closer to 50% by the end of the year.

“These are some of the hardest tasks in the economy that people pay millions of dollars to consulting firms to do, and the models are finally being able to do them with an incredible rate of progress,” Foody said.

AI has already disrupted the consulting industry, changing the way firms hire and make money, but the likelihood of agents displacing consultants grows as the models continue to improve.

McKinsey chief Bob Sternfels recently said the prestigious management consulting firm had 60,000 employees, 25,000 of which were AI agents.

Sternfels recently said it’s the first time in McKinsey’s history that the company is able to grow without growing its head count.

Where AI agents fail in consulting tasks

The frontier models Mercor tested included those from OpenAI, Google, and Anthropic, among others.

One example consulting task instructed the AI agent to “analyze category consumption patterns and market penetration using the Category Penetration Score methodology for PureLife’s portfolio strategy,” asking for several specific outputs in response. The AI agents failed to produce an accurate response.

“No model is ready to replace a professional end-to-end,” the findings concluded.

Mercor found the AI agents were great at research and pretty good at data analysis, Foody said.

Where they consistently got tripped up was on longer-horizon tasks — the longer it would take a human to complete a task, or the more steps it took, was the biggest indicator that the model might have a hard time.

Unlike a human, Foody said, the models struggle to understand where in a specific file system they should look for the right information, so they often end up looking at the wrong files. They struggle with the planning side of figuring out how to work with multiple tools and cross-referencing files at the same time.

For tasks that can be done in an hour or less or that only require the use of a single tool, the models perform relatively well.

Foody said the agents are almost like interns, where they might have a 50% pass rate, and the partner is still noticing a lot of issues in the work.

Frank Jones, a former KPMG consultant who now works as an expert contractor for Mercor, said in his experience training AI, he’s found the models can get close at certain tasks, but that some human refinement is often needed.

He also said the models need very specific prompts because they don’t always understand common expectations or phrases in consulting, like “client-ready.”

“Most consultants, they know what that means. But for AI, I think there’s a lot of nuance in that,” he said.

The AI models are quickly improving

According to Foody, continuing to improve the models doesn’t require a breakthrough — it requires more and better training, which the frontier labs are already investing heavily in.

“That’s why we have so much revenue,” he said, adding, “We’re in the business of replacing human judgment.”

Mercor, whose clients have included OpenAI, Anthropic, and Meta, secured a funding deal in the fall that valued the company at $10 billion. Mercor employs more than 30,000 contractors around the world who help train AI models through tasks like rewriting chatbot responses. Foody previously said the company grew its revenue in 2025 by 4,658%.

Foody said he believes consulting, and especially lower-level roles, are among the jobs he’s confident will be displaced by AI. He said the next version of the AI agents benchmark will expand to evaluate the whole value chain of a professional services firm: “Instead of evaling the analyst, we’re evaling McKinsey itself.”

Right now, he says Mercor’s AI agent benchmark tells an appealing story for McKinsey, because the company could say it shows they can use AI to add value but not replace humans.

“The next version of APEX tells a very scary story for McKinsey,” he said, adding, “In the coming two years, we’re going to have chatbots that are as good as the best consulting firm.”




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A Nobel Prize-winning physicist explains how to use AI without letting it replace your thinking

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Think AI makes you smarter?

Probably not, according to Saul Perlmutter, a Nobel Prize-winning physicist who was credited for discovering that the universe’s expansion is accelerating.

He said AI’s biggest danger is psychological: it can give people the illusion they understand something when they don’t, weakening judgment just as the technology becomes more embedded in our daily work and learning.

“The tricky thing about AI is that it can give the impression that you’ve actually learned the basics before you really have,” Perlmutter said on a podcast episode with Nicolai Tangen, CEO of Norges Bank Investment Group, on Wednesday.

“There’s a little danger that students may find themselves just relying on it a little bit too soon before they know how to do the intellectual work themselves,” he added.

Rather than rejecting AI outright, Perlmutter said the answer is to treat it as a tool — one that supports thinking instead of doing it for you.

Use AI as a tool — not a substitute

Perlmutter said that AI can be powerful — but only if users already know how to think critically.

“The positive is that when you know all these different tools and approaches to how to think about a problem, AI can often help you find the bit of information that you need,” he said.

At UC Berkeley, where Perlmutter teaches, he and his colleagues developed a critical-thinking course centered on scientific reasoning, including probabilistic thinking, error-checking, skepticism, and structured disagreement, taught through games, exercises, and discussion designed to make those habits automatic in everyday decisions.

“I’m asking the students to think very hard about how would you use AI to make it easier to actually operationalize this concept — to really use it in your day-to-day life,” he said.

The confidence problem

One of Perlmutter’s concerns is that AI often speaks with far more certainty than it deserves and can be “overly confident” in what it says.

The challenge, Perlmutter said, is that AI’s confident tone can short-circuit skepticism, making people more likely to accept its answers at face value rather than question whether they’re correct.

That confidence, he said, mirrors one of the most dangerous human cognitive biases: trusting information that appears authoritative or confirms our existing beliefs.

To counter that instinct, Perlmutter said people should evaluate AI outputs the same way they would any human claim — weighing credibility, uncertainty, and the possibility of error rather than accepting answers at face value.

Learning to catch when you’re being fooled

In science, Perlmutter said, researchers assume they are making mistakes and build systems to catch them. For example, scientists hide their results from themselves, he said, until they’ve exhaustively checked for errors, thereby reducing confirmation bias.

The same mindset applies to AI, he added.

“Many of [these concepts] are just tools for thinking about where are we getting fooled,” he said. “We can be fooling ourselves, the AI could be fooling itself, and then could fool us.”

That’s why AI literacy also involves knowing when not to trust the output, he said — and being comfortable with uncertainty, rather than treating AI outputs as absolute truth.

Still, Perlmutter is clear that this isn’t a problem with a permanent solution.

“AI will be changing,” he said, “and we’ll have to keep asking ourselves: is it helping us, or are we getting fooled more often? Are we letting ourselves get fooled?”




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