I woke up to a WhatsApp message at 5:04 am.
“Are you there? One more.”
I felt the knot in my stomach immediately. I jumped out of bed and replied right away. “I’m here. Who?”
It was a rainy morning in late October 2025, near the end of my sixth year working for Amazon’s headquarters. I was on a contracts team where most of the lawyers were from the US, a few were from India, and I was the only lawyer from the EU.
On Tuesday that week, an email went out to everyone at Amazon: 14,000 corporate positions would be eliminated. Then people started receiving emails or phone calls, one by one. They were disconnected immediately, their badges deactivated.
Those of us who stayed didn’t know who had left, or whether we were next.
It was by far the worst week of my corporate career.
And I felt guilty, too.
Two years earlier, I had started working on contract review automation project. It grew into an AI bot, then took on generative AI, and the tool kept getting better. It became one of Amazon’s first generative AI success stories and was featured in the Wall Street Journal. I received an award for it from a VP during a visit to Seattle.
But that October it ate me up from inside. Part of my job was to show how much time the tool saved. Everyone was talking about AI bringing layoffs, and I thought: you need to stay on top of it. If you learn to work with AI, your job won’t be eliminated.
However, the jobs of your colleagues can.
That’s what I didn’t see then.
When the layoffs hit, they sent me spiraling through so many emotions. Was it my fault? Could I have prevented it? Or was I too small a part of this, and it would have happened with or without me?
I guess I’ll never know.
I resigned two months later.
Today I work in a regulated industry in the EU which is a very different environment. Processes are different. Approvals are more measured.
Last week, two things happened at once and they made me think about my time at Amazon.
I took part in a round table discussion in the Slovak AI community, where we talked about AI adoption, AI transformation and what’s blocking it. We talked about layoffs too. I couldn’t help noticing how different the approach was. Several companies described slow processes and waiting for approvals from parent companies in other EU countries. Heavy regulation on top of the AI Act. And layoffs? Not anytime soon. The mood in the room was that AI can’t replace us yet.
When I got home, I opened LinkedIn and a Business Insider article popped up: Amazon is trying to rehire workers it laid off. Mostly for AI and cloud roles.
But Amazon wasn’t the only company cutting jobs in 2025. Meta, Microsoft and Google did too. Did US companies misjudge the speed of AI adoption?
In the EU, all of these decisions are still ahead of us.
Which made me wonder: is the EU behind on AI?
Level at the Start
If the question is whether European companies have started using AI, the EU isn’t behind. In 2025 the European Investment Bank asked firms on both sides of the Atlantic the same question. 37% of EU firms were using generative AI. In the US, it’s 36%.
Eurostat counts differently and lands lower: 20% of EU companies with ten or more employees used some form of AI in 2025, up from 8.1% two years earlier. Among large companies it’s 55%. The spread inside the EU is wider than the one across the Atlantic, with Denmark at 42% and Romania at 5.2%. Slovakia, where I’m from, sits at 18%.
So the starting line is roughly level.
Behind After That
The same EIB survey asked how far AI had spread inside companies that were already using it. 81% of US firms had it in more than two business activities. In the EU, it’s 55%.
Workers show the same pattern. In a paper published by the St. Louis Fed, economists surveyed workers in the US and six European countries (the UK, Germany, France, Italy, Sweden and the Netherlands) with identical questions. 43% of American workers use AI for work, against 32% in the European countries. Americans spend 5.2% of their working hours with it. Europeans spend between 1.5% (France) and 2.8% (the UK).
Insurance shows what that looks like in one sector. When EIOPA surveyed 347 insurance undertakings, nearly two-thirds were using generative AI. And yet: “Most undertakings are nevertheless still at a proof-of-concept stage.” The supervisor calls it “a considerate, controlled roll-out.” Anyone who has worked in a large company has a less diplomatic word for it.
Then there's the money. According to the Stanford AI Index, private AI investment in the US reached $285.9 billion in 2025. Europe as a whole: $20.9 billion. The US invested almost 14 times as much, and the gap is growing. US investment rose 160% on the previous year, Europe's rose 7%.
It looks like European companies aren’t refusing AI. They start, run the pilot, and then it slows.
Mostly, It’s How Companies Are Run
The St. Louis Fed paper breaks the US–Europe difference in worker adoption into pieces.
About 55% of it is simply what the economies are made of: which industries, which jobs, what size of company, how old and how educated the workforce is. Small firms adopt less everywhere, and Europe has far fewer large tech companies. Comparing Amazon with a European bank or insurer compares two sectors as much as two continents, and the paper puts a number on how much of the difference comes from that kind of thing.
The rest comes down to one question: does your employer encourage you to use AI and give you the tools to do it? 42% of US workers get both. In France and Italy, 16 to 17%. Once the researchers added encouragement to their model, “almost all of the US-Europe adoption gap is accounted for.” The paper also found that “AI training does not predict higher adoption once we control for encouragement and tool provision.” Training on its own, without permission and a tool to use, doesn’t move the number.
The paper doesn't measure approval processes. But while a tool waits for approval, employees are being told "not yet", which is the opposite of encouragement.
What the AI Act Did
The AI Act entered into force on 1 August 2024. The first obligations that apply to companies using AI, AI literacy and the prohibited practices, started in February 2025. The AI Act mostly didn’t slow anyone down by what it required. It slowed them down by what it was about to require.
For banks and insurers, that was concrete. Creditworthiness assessment and credit scoring of individuals, and risk assessment and pricing for individuals in life and health insurance, are on the Annex III high-risk list, and those obligations were due on August 2, 2026. Many regulated companies spent 2025 preparing for a date that was written into law. Then the Digital Omnibus moved it to December 2, 2027. The delay entered into force on July 27, 2026, six days before the original deadline. Before that came months of not knowing whether the delay would happen at all, which slows a project down in its own way.
Legal uncertainty in general shows up in the data. Among EU firms that looked at AI and decided against it, 53.6% said they couldn’t tell what the legal consequences would be. That group is small: roughly 6% of all EU companies with ten or more employees. “Legal consequences” can also mean the GDPR, liability and copyright, not only the AI Act. The top reason, at 70.3%, was lack of expertise.
I’d like to stop there, but a September survey by Bitkom, Germany’s digital industry association, doesn’t let me. It covers German companies with 20 or more employees and asks a different question from Eurostat, so the numbers don’t compare directly. 57% of German firms now use AI, up from 20% two years ago. And 66% say the AI Act brings more disadvantages than advantages, up from 56% last year. Bitkom’s president said that the AI Act should build trust, but it creates widespread uncertainty.
Companies are adopting AI and resenting the AI Act more this year than last. Perception isn’t proof of cause, and the complaints grew in the same year that adoption jumped from 36% to 57%. But it would be dishonest of me to say the AI Act plays no part. It plays a part. But the evidence says it’s not the main one.
It’s also been blamed for things it had nothing to do with. The most visible “EU blocks AI” story this year was Apple holding back Siri AI in the EU, citing the Digital Markets Act. The Commission’s reply: “The decision not to roll out Siri AI in the EU is Apple’s and Apple’s only.” Whoever is right, the AI Act wasn’t in that fight.
The Brakes That Aren’t in the AI Act
In financial services, most of the approval chain existed before the AI Act. Third-party risk, outsourcing and data protection reviews run on every new tool, AI or not. An AI tool bought from a vendor is, before anything else, a new vendor.
Group structures add another layer, in any sector. A subsidiary of a group headquartered in another country often needs group sign-off on vendors and security on top of its own reviews. Each review is reasonable on its own. Run one after another, they add up.
Some brakes are national. In Germany, employers with a works council generally need its agreement before rolling out a company tool that could be used to monitor employees, and many workplace AI tools could be. A Hamburg labor court found no such right when staff used ChatGPT voluntarily through private accounts the employer couldn’t see (a first-instance decision, in interim proceedings). The same tool, provided and paid for by the company, is a different conversation. Most American rollouts have no equivalent step.
And some of it is simply how decisions get made. In his 2016 shareholder letter, Jeff Bezos split decisions into two kinds. Some are irreversible and deserve care. But “many decisions are reversible, two-way doors. Those decisions can use a light-weight process.” He added: “If you wait for 90%, in most cases, you’re probably being slow.” I have lived by those at Amazon.
Regulated companies tend to send every AI tool through the process built for irreversible decisions. However, an internal drafting assistant should not get the same review as a credit scoring model.
What to Do With This
If you sit in the approval chain, sort what’s waiting into two piles: the tools you could switch off tomorrow without harm, and the ones you couldn’t. They don’t need the same process, and you don’t want the first pile end up waiting behind the second.
Separate the AI Act question from the vendor question. For most internal productivity tools, the AI Act question is short. They aren’t Annex III use cases, so the high-risk rules won’t apply to them in 2027 either, unless you start using them for something on the list, like screening job applicants or evaluating employee performance.
The second one is the realistic trap: a meeting summarizer whose notes start feeding performance reviews has drifted onto the list, and a deployer who changes a tool’s purpose that way can become its provider. What applies today is AI literacy, the prohibitions (including the ban on emotion recognition at work) and, in specific cases, the transparency duties in Article 50. The vendor, security and data protection reviews are longer, and they would run anyway.
Don’t let December 2, 2027 govern tools it doesn’t apply to. The high-risk deadline is real for credit scoring and insurance pricing. For a meeting summarizer, it’s not.
If you sell AI into regulated European companies, the long sales cycle is mostly your buyer’s vendor-review machinery, not their AI Act anxiety. Arriving with the answers that machinery asks for (where the data goes, who your subprocessors are, how you handle incidents) will likely shorten it more than any AI Act compliance badge.
Does Slower Mean Fewer Layoffs?
From March to July, AI was the most common reason US companies gave for cutting jobs. Challenger, Gray & Christmascounted more than 116,000 announced cuts attributed to AI in the first eight months of 2026. In August, AI dropped to fourth place, and hiring plans were up 37% on last year. As Andy Challenger put it in July: “AI-related cutting has been limited outside of the Tech sector.”
Many of those cuts may be premature. A Harvard Business Review survey of 1,006 executives found that AI-related layoffs are “almost completely in anticipation of AI’s impact,” not a response to what AI has actually delivered. The authors warn about companies “having to rehire in embarrassing retreats.”
So did US companies misjudge the speed of AI adoption?
Some of them, it seems, cut for the AI they expected rather than the AI they had.
Stanford researchers using payroll data found that employment of 22- to 25-year-olds in the most AI-exposed occupations now sits about 19% below the trend of workers the same age in less-exposed jobs. That’s driven by fewer young people being hired, not more people being fired.
Europe has no count comparable to Challenger’s, which isn’t the same as having no cuts. Lufthansa has announced 4,000 administrative job cuts by 2030 and pointed to AI. On average, though, the ECB finds that “companies that make significant use of AI are about 4% more likely to take on additional staff.” Only 15% of firms using AI cite cutting labor costs as a reason.
European labor law slows firing. EU rules on collective redundancies require consultation above certain thresholds, and national dismissal rules add more. None of it applies to hiring. Not hiring a graduate needs no consultation.
Slower adoption may mean fewer AI layoffs for now. A slower European company might avoid the fire-then-rehire mistake. Or it might make the same mistake later, in a different form: fewer graduates hired, and no one fired.
The last time Europe was slow with a technology, the cost wasn’t measured in jobs. The Draghi report found that, “excluding the main ICT sectors,” EU productivity “has been broadly at par with the US in the period 2000-2019.” The difference was the tech sector. It cost Europe productivity growth, and the companies: “only four of the world’s top 50 tech companies are European.”
Whether AI goes the same way, I don’t know. I do know that usually no one gets fired over an approval that took months. That’s what makes it easy to keep waiting.
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So you did have an answer
Thank you for sharing the Amazon story so openly; it gives the numbers real weight. The EIB figures (37% vs 36%) suggest adoption isn't the gap, it's what happens after the pilot. From what I see, the slowest approvals in European companies are less about the AI Act itself and more about unclear answers on where data goes and who can access it. Did the Slovak round table read the parent-company approval loops as legal caution, or as uncertainty about the vendors?