What's truly terrifying isn't the layoffs, but the fact that they aren't hiring anymore.

Thu, 16 Apr 2026 02:18:44 +0800

Seeing Block cut its workforce by 4,000 people out of a group of over 10,000 at the end of February 2026 really shook me. I’ve always worked in financial IT—things like trading pipelines, Hong Kong/US stocks, and system fundamentals. Usually, I’m accustomed to buzzwords like “efficiency improvement,” “automation,” and “cost reduction while increasing efficiency.” But when a fintech company, one so close to money, compliance, and risk control, publicly cites AI as the reason for layoffs, it still hits you hard emotionally.

My current assessment is very direct: the scariest part of AI layoffs isn’t a layoff list in the news one day, but when companies start assuming that “smaller teams can do more work.” This means no backfilling for departures, fewer entry-level positions, and much tighter headcount management. From April 2025 to April 2026, this wind is still blowing in the US; while China hasn’t seen a high-profile, public wave of mass layoffs yet, the quiet squeeze has already begun.

Let’s look at these most representative companies first

Block was the harshest and most direct this time. After the Q1 2026 earnings report, Jack Dorsey’s statements left almost no room for ambiguity: AI has changed how the company builds and operates, allowing smaller teams to accomplish more. AP reported that Block laid off over 4,000 people, accounting for about 40% of its workforce of over 10,000 employees. The market was also honest; it rose by over 20% pre-market at one point. Why? Because investors were hearing not “technological ideals,” but profit margins, operating leverage, and faster earnings growth. Earlier on, Block had set targets during its Investor Day in November 2025 for a 17% increase in gross profit and over 30% growth in adjusted operating income for 2026. Simply put, this wasn’t about “laying people off because the company is failing,” but rather the company believing it could grow while simultaneously slimming down its workforce.

Duolingo, on the other hand, represents a different approach. At the end of April 2025, the company publicly shifted to an AI-first roadmap, stating that it would gradually phase out outsourced work that AI can handle, and any new hires must first prove that AI cannot do the job. A few days later, it launched 148 new courses created with generative AI. By August 2025, Duolingo’s earnings report showed a 41% year-over-year revenue increase in Q2, with strong profitability and subscription revenue, leading to a nearly 30% jump in stock price that day. However, the company also provided another lesson: users are not entirely convinced. The CEO later admitted that the backlash on social media indeed suppressed growth to the lower end of the guidance range. In other words, while AI can help a company scale its content production and boost profits, brand and trust are not freebies.

Klarna is perhaps the most typical example of “AI pressuring labor costs” over the past two years. It stated in 2024 that its AI customer service assistant handled two-thirds of inquiries, equivalent to the workload of 700 full-time customer service agents. In its prospectus submitted in March 2026, Klarna made the calculation even more explicit: at the end of 2023, there were 4,352 full-time employees; by the end of 2024, it was 3,422; and by the end of 2025, it would only have 2,831 people. The company itself stated that the reduction in headcount came from “using AI to improve efficiency and proactively compressing overall headcount.” Meanwhile, the average revenue per employee in 2025 was projected to rise from $344,000 in 2022 to $1.24 million. This figure is quite alarming. However, Klarna later began bringing some complex customer service issues back to human agents, especially those with high customer value and high risk scenarios. The lesson here is very clear: AI is excellent at absorbing standardized, repetitive, and process-driven tasks, but when it comes to complex judgment, trust endorsement, or gray-area responsibility, humans still need to be there as a fallback.

Shopify, however, has articulated something even scarier. In April 2025, Tobi Lutke’s memo to employees was very direct: before applying for more headcount, teams must first prove why the job cannot be done entirely with AI. It might not make daily headlines about layoffs, but it is creepier than a layoff list. Because it means that entry-level positions and roles that can be standardized and broken down are blocked at the source.

Why the Market Applauds

The capital markets love hearing AI stories; this is something that no longer needs to be hidden.

The reason is simple:

Revenue is not dropping, and is even growing.
Personnel costs will drop first.
Per capita output looks better.
The profit margin and cash flow potential in the future are much larger.

So, Block’s stock price will immediately rise because of the “smaller team can do more” narrative, Duolingo will receive good earnings feedback despite being criticized in the public eye, and Klarna will weave AI and efficiency into its IPO narrative.

Of course, there is real efficiency gain here, but there are also obvious elements of AI washing. Many companies aren’t seeing a scenario where some model suddenly becomes so smart that it can eliminate an entire department. Instead, management has finally found a nice-sounding reason to give to investors: to scale back the headcount expanded over the past few years, stop replacing people who naturally leave, and have AI work alongside fewer people on tasks like outsourcing, customer service, operations, content production, and low-level analysis.

How should I put it, AI is both a tool and a banner here.

Is this wind still in the US now?

It’s still there, and even more so by March 2026, the wind is stronger than in 2025.

Data from the US layoff tracking firm Challenger, Gray & Christmas speaks volumes:

In 2025, the number of layoffs citing AI as a reason reached 54,836 people.
In October 2025, AI was already the second largest cited reason for layoffs that month, corresponding to 31,039 positions.
By March 2026, AI became the top reason for layoffs cited by US companies for the first time that month, corresponding to 15,341 positions, accounting for about a quarter of the layoff plans disclosed that month.

But this dataset also needs to be looked at from another angle. It shows that AI layoffs are real, not just a joke; it also shows that we haven’t reached the point where “all layoffs are due to AI replacement.” Since Challenger started tracking this metric in 2023, those explicitly attributed to AI layoffs only account for a small fraction of all announced layoff plans. In other words, the US currently seems to be a combination of two forces:

One wave is genuine automation replacement, first hitting customer service, content, localization, junior white-collar workers, and back-end processes.
The other wave is macro environment, cost pressure, management’s narrative of contraction, topped with an AI veneer.

So, if you only look at news headlines, it’s easy to feel that “AI has eliminated white-collar jobs entirely”; but if you only look at official statements, it’s also easy to underestimate how it has begun rewriting hiring and organizational structures.

In China, why hasn’t it become like the US?

I tend to believe that it’s not that China hasn’t been affected, but rather that the way the impact manifests is different.

From the public narrative, China in 2025 to 2026 seems more like “scrambling for AI talent” rather than “publicly laying off people due to AI.”

According to a report by Xinhua News Agency at the end of March 2025, the job openings for algorithm engineers and machine learning positions in February saw year-on-year increases of 46.8% and 40.1%, respectively, indicating a significant supply shortage with an AI talent supply-demand ratio of approximately 3:1. By February 2026, another article from Xinhua News Agency mentioned that the hiring volume for AI-related positions in China in the fourth quarter of 2025 was still growing year-on-year by 19%. In terms disclosed by the Ministry of Industry and Information Technology in March 2026, the core AI industry scale in China in 2025 had already exceeded 1.2 trillion yuan, with related enterprises surpassing 6,200.

This set of data at least suggests two things:

China’s current public main themes are still industrial expansion, model implementation, and shortage of AI talent.
What is truly scarce is talent that combines algorithms, engineering, and industry, not ordinary white-collar office workers.

But this doesn’t mean there is no pressure.

Domestically, it’s more like a quiet squeeze:

Outsourcing is contracting first.
Hiring for campus recruitment and entry-level positions are more cautious.
Standardized roles such as Operations, Testing, Content, and Customer Service are being compressed first.
Some miscellaneous tasks that originally required two or three people to collaborate are now defaulting to one person working with AI.

So you might think, “Why haven’t I seen headlines about mass layoffs due to AI in China?” But the entry points for many positions have narrowed, they just haven’t been packaged as a public letter for investors.

Financial IT Development, What to Be Most Wary Of Isn’t Actually “Being Replaced Tomorrow”

I also work in financial IT, so I have a stronger feel for this part.

In the short term, development on core links within the financial industry—especially areas like trading, clearing, risk control, compliance, monitoring, and low-latency communication—are unlikely to be completely replaced by AI. The reason is simple: the stakes are too high, auditing is too strict, and the cost of an anomaly is too great; if something goes wrong, it’s not something you can fix just by changing a prompt.

But on the other hand, it is also very realistic:

People who write “glue code” will feel the pain first.
The value of pure CRUD, pure API lifting/moving, and pure documentation organization will continue to decline.
Tasks like testing, operations scripts, reporting, and internal tools will increasingly be assumed to be done with AI assistance (“you bring AI and finish it together”).
The requirements for a team’s individual combat capability, business understanding, and upstream/downstream connection ability will become higher.

To put it plainly, the most dangerous thing in the coming years might not be “AI replacing developers,” but rather that “a person who is proficient with AI and understands business boundaries will be considered equivalent to the output of two or three people from before.” If a developer only remains capable of rote coding tasks, without mastering the business domain, understanding risks, or taking ultimate responsibility, they will indeed become increasingly passive.

The Last, Less Pleasant Judgment

The news about Block shook me, not because it represents that AI is mature enough to completely take over fintech companies, but because it represents that management and the capital market are willing to say this, and the market is willing to believe it.

This is the most critical point.

Technology maturity, maybe not to that exaggerated extent yet; but organizations will first change based on “it’s almost good enough,” hiring will first change based on “it’s okay if we hire fewer people,” and promotions and performance reviews will also first change based on “why didn’t you use AI to do more.”

For average developers, especially in roles like financial IT that are caught between business needs, compliance, and system stability, what needs to be done next is not to fight AI head-on, nor is it to be blindly optimistic. Instead, it’s about quickly shifting oneself from being a “code laborer” toward becoming someone who “understands the business, understands the risks, knows the boundaries, and can provide fallback support.”

Otherwise, the wind from America will blow over eventually. Even if it’s not with the same news headlines, the pressure will fall on the same people.

References

Writing Notes

Original Prompt

Compile the layoffs over the past year caused by AI, large-scale layoffs, the company’s operating status after the layoffs, market feedback, and whether the layoff trend is still present in the US or if it has little impact on mainland China. I myself am a developer in the financial IT industry, and the news of Block’s layoffs greatly shocked me.

Writing Outline Summary

Start with the Block news to maintain the personal perspective of “Financial IT developers are shaken.”
Instead of attributing all layoffs crudely to AI, break it down into three layers: “public layoffs,” “hiring freezes,” and “entry-level roles being squeezed.”
Select four representative examples—Block, Duolingo, Klarna, and Shopify—to examine layoffs, hiring freezes, operational results, and market feedback for each.
For the Chinese section, instead of stating it’s “nothing to worry about,” frame it as “the layoff wind isn’t as strong in China as in the US, but structural squeezing has already begun.”
Conclude by bringing the assessment back to the real situation of financial IT development; the focus should not be on emotional appeal, but on the changing professional structure.

Layoffs on Uncle Xiang's Notebook