The AI rehiring panic everyone got wrong

We all know that roughly 120k tech roles has been cut in the year 2026 and AI was the most cited reason among all.

Aug 16, 2026

The AI Layoff Bill Is Coming Due, And CTOs Are Going To Pay It Twice : r/technology

You may have hearing or reading that AI is backfiring or Companies bringing the engineers back or AI is failing or The humans are coming back etc etc. Trust me I am reading these for a quite long time. And I start wondering, is it really?

So being a nerd I started digging deeper and here is what I found.

The trend is divided

Here is the scale of things everyone is talking about.

We all know that roughly 120k tech roles has been cut in the year 2026 and AI was the most cited reason among all.

Read this report by Challenger, Gray and Christmas if feel otherwise.

That report mentioned all the tech giants and their layoffs as they were announced.

PayPal, Microsoft, Meta, Google Amazon and everyone else who cut their workforce in those times marked as AI specific layoffs. But if I look closer that line is still trending downward and companies still laying off their workforce.

So, I started focusing on the other line which is smaller but louder. Here are some context before I get into it

The Orgvue survey tells us that 39% of business leaders made people redundant because of AI and 55% of those now admit that the decision was wrong.
Robert Half mentioned that thirty two percent of the hiring managers from US have already rehired someone for a role they cut because of AI.
Forrester is predicting that half of all AI attributed layoffs will eventually be quietly reversed.

So, I can understand why the conclusion you have made seems backed by the data itself. The industry is still in layoff mode and some of those layoffs are being rehired as well both are true at the same time.

But the story has a lot more to say, actually there are three different failures and each one has a specific engineering cause behind it. Putting them together as AI is failing misses the actual lesson in each case and that is the only part worth keeping for our own deployment decisions.

Failure 1 - The undocumented knowledge

Read this story

Ford rehired and promoted more than 350 engineers.

But what matters is, the why?

The knowledge these engineers has to run design reviews before vehicles reach production were undocumented. They knew the failure patterns which they have experienced, the edge cases which was never logged and things which was never written down but was important. All these existed only in their mind.

So, when Ford trained their system on available data (service records, design specs, test logs etc) was an incomplete version of what those engineers actually knew. And they found that out in a hard way.

Ford’s executives estimated this knowledge worth hundreds of millions to help catch those issues early.

This is not a story where AI is not able to understand vehicle design, it is all about the undocumented knowldge.

I have experienced this first hand with obviously a less expensive consequences.

Every Knowledge Graph I have shipped runs into the same undocumented knowledge wall. The system is only as good as what was recorded and available. The most valuable knowledge in most of the organization are the ones live in a senior person’s head.

Buying better models cannot fix this, you only fix it by writing down the undocumented knowledge before the AI project starts.

Failure 2 - The remaining 6%

Using AskHR (An AI Agent) IBM resolves 94% of the employee request in their HR function without a single human in the loop. That is commendable result and nobody can deny that.

But the remaining 6% is where the actual story is. IBM describe that as Ethical Dilemmas and Complex Situations which does not fit into their policies as cleanly as it was needed. Simply means those requests needed some judgement instead a lookup into the database.

So, IBM does not gave up the their AI Agent but it was to announce that they would triple entry level hiring across their US business in the year 2026.

Another example where Commonwealth Bank of Australia faced the similar problem but in different angle.

They laid off more than 40 of their customer service staff and replaced them with AI Voice Bot. The Bot could not handle the call volume and the cases where human judgement was needed. They reversed the layoffs and admitted that they had not consider all the relevant business cases.

Both these cases are not a capability failures, AskHR is resolving 94% of the cases correctly, the CBA bot was able to process most of the routine calls. What went wrong was the boundary, when they deployed the system without a precise definition of what was suppose to do in those specific scenarios.

The 6% number is completely depends on the judgment specific worjloads which training data cannot encode by itself.

Failure 3 - The single metric trap

I hear a lot about Klarna case and I feel that everybody only reads the headline, if they go past that they would know.

In the year 2024 Klarna’s OpenAI powered Agent handled 2.3 Million customer service conversations in its first month and cut down the resolution time from 11 Min to under 2 Min. The company projected A total of $40 Million in savings that year.

But, within 18 Months they started rehiring and headlines framed this as a reversal. Actually it was a rebalancing, the AI Agent is still in the front lines for the high volume routine tier support. The human agent was to serve the premium tier for complex cases which involves trust, emotion and risk where resolution time is not that important.

Look what the CEO has said about this whole situation. It fits into a clear case of The single metric trap as they were only focused on volume and resolution time and has not consider the customer satisfaction etc in the beginning.

If we pick an easy number to optimize for example resolution time, cost per ticket, tokens per task etc. The data will support you for it but you also need to look at the things you are not measuring, churn, complaints, warranty claims etc. The fix in such scenarios are quite higher then the original savings.

At the end, this case of Klarna does not prove that AI cannot do customer service, it proved that fixing and optimizing for a single metric will eventually fail.

What we should learn from this cases

Each of this headlines and cases are there to teach us something and it only become clear when we dig deeper and beyond the headlines

The five year clock

If you look at the IBM’s case, their CHRO said that investing in entry level hires is a must otherwise in 3 to 5 years there will be no pipeline and the well simply dries up. They are doing headcount calculations on a longer clock then completely relying on AI only solution.

So if we cut that intake today as AI can do the entry level work, we are not eliminating the cost but deferring it into the future with a bigger number.

I myself has scaled engineering org from 3 to 130 people and I have been on the other side of this exact decision to hire or automate. One thing I have learned in the hard way that automating everything is not possible and we need people to manage the complex workflows and systems.

The Duolingo’s case

You may have read that Duolingo’s CEO announced an AI First memo in the April 2025, he stated only hire if a team cannot automate more of its work. He met with immediate backlash, 1000s of comments with 600 reposts and even threats to delete the app. This story and the coverage on it was very wrong in every way that matters.

In fact, Duolingo never laid off a single full time employee. They even keep hiring in the same year the Memo went public.

In the year 2023 and again in 2024 they cut down 10% of their contract workforce of translators and writers. In May 2026 they backtracked on their internal policy to evaluate employees on their AI usages due to employees kept asking if they must use the tools for the sake of using them.

But, the headline said that AI first company reverses course on AI. And the fact was company never touched full time headcount and clarifies on their internal policy after the employee push back.

So, this story was never about employees or AI failing but contractors are being replaced with the internal systems.

Conclusion

Here is what I got and think about this whole AI is backfiring narrative

The rehiring stories are real and each one of them has a specific case to learn. None of them are evidence that AI does not work and cannot do the tasks.
Shopify’s headcount has been very flat and its not because of AI failed but they decided to keep a flat headcount. PayPal, Microsoft and Meta are still laying off and data does not show they are wrong.
The tech headcount in 2026 is still declining and roughly 120k jobs has been cut and not comparable against the rehires.

These stories also gives us a checklist which we can run before we go about deploying AI in our workflow

Make sure you have all the knowledge each person holding before they leave.
Not everything can be automated or offload to AI, keeping key persons become important.
Pick a mixed of metrics which includes quality before you optimize the easy ones.
Plan for at least 5 years on headcount so that you have an active pipeline.