In the AI era, the old model of technical support is collapsing. This isn’t bad news — it’s the start of a new era.
I Got Laid Off, But I Felt Relieved
It’s been over two months since I was laid off. In the past, I might have felt anxious, panicked — but now, inside I actually have a strange sense of release.
Not because the severance was good (though it really was), but because I can finally set down those old working patterns that are being made obsolete, and go all in on embracing the new paradigm of the AI era.
The Twilight of Traditional Tech Support
Let’s first talk about the essence of the traditional tech support role.
Over the past dozen-plus years, what has been the core logic of tech support? Pile on people, soothe, fight fires.
1. Pile on People
Customer has a problem? Add people! User inquiries went up? Recruit more! Can’t understand the product docs? Assign dedicated one-on-one guidance!
The core assumption of this model is: people’s time is cheap, and you can solve everything by adding manpower.
But there’s a fatal flaw here: people grow linearly, while problems explode nonlinearly. When the product’s user base grows tenfold, you may need to hire twenty times the people. The ROI keeps dropping, the marginal cost keeps rising.
2. Emotional Value
Tech support doesn’t just have to solve problems; it also has to provide “emotional value.”
“Dear, I completely understand how you feel““
“Please don’t worry, I’ll take a look for you right away
“Sorry for the trouble, I’m truly sorry~”
The existence of these scripts is essentially a fig leaf for a product experience that isn’t good enough. Because the product is hard to use, the docs are obscure, and the API design is anti-human, you need human “attentive service” to make up for it.
But from another angle — if the product itself were simple enough, the API clear enough, and the docs thorough enough, would users really need all this “emotional value”?
3. Firefighting
“Production had an incident!”
“The client’s boss is blowing up in the group chat!”
“It has to be fixed right now, or we can’t sign the contract!”
This firefighting mode of handling things keeps tech support perpetually passive. You never know what pit is waiting for you to fill in the next second, you’re always cleaning up other people’s messes, and you never grow. Even though I’ve always done a lot of automation — which had value — in the AI era it really isn’t enough anymore.
This Judgment Is Most Likely Correct: The Tech Support Role Was Designed as a “Consumable”
Before we talk about how AI changes everything, I want to confront a more fundamental judgment: the tech support role is, structurally, designed to be a “consumable.”
First, it’s essentially a “human buffer pad”
When the product has problems, the process has problems, the docs have problems — in the end it’s tech support that shoulders it. The thing you spend huge amounts of time solving isn’t a technical challenge; it’s the repeated confusion of “why can’t the customer find this button.” Your professional judgment, in this scenario, gets compressed into two numbers — “resolution rate” and “satisfaction” — and on those two numbers, a 25-year-old and a 35-year-old can’t open any essential gap.
Second, technical depth is locked shut
What you deal with is the “surface symptoms” of the product — errors, configuration, usage questions — not the “pathology”: architecture design, code logic, business decisions. You’ve “treated” many “illnesses,” but you’ve never been in the “operating room.” Three years of this kind of experience and ten years of it aren’t very different, because the underlying knowledge doesn’t iterate.
Third, the physical toll of emotional labor is invisible
Not the physical toll of moving boxes, but the physical toll of emotional labor — facing angry customers, pointless bickering, departments passing the buck internally, while staying polite and professional long-term. This kind of drain wears a person down more than writing code, and it doesn’t show up on a résumé as a “core competitiveness.”
Fourth, the lowest status — anyone can boss you around
Sales talks down to you, customers curse you, R&D dumps blame on you, and leadership only leans on you to take the fall. Neither up nor down, a sandwich cookie — purely taking the hits and wiping other people’s asses, with zero say.
Fifth, the exit is extremely narrow
To move up, you either go presales (needs sales ability) or product (needs a business perspective and internal opportunities) or implementation/ops (which is often just another pit). If you stay put and become a senior tech support person, many companies don’t even have such a level — or they give you the title but not the resources, and past 35 the pricing logic is no different from a junior support person.
In the company’s eyes, tech support is a cost department, not a revenue department. There’s no money to split with you, no promotion path; work until you’re old and you’re just a senior support person — there’s basically no way over to management or R&D.
Sixth, replaceability maxed out, a pure standardized consumable
It doesn’t need much talent or deep skill — know a bit of basic ops and system operation and you can do it. A completely competitive role, people are a dime a dozen, salaries can’t rise, premium potential is zero, and the boss can swap you out any time — he simply doesn’t lack for you.
So when you say this role is “garbage,” it’s not an emotional vent, it’s an accurate assessment of the role’s economic value. It’s a typical low-end consumable role that sells time for living expenses — no growth, no pricing power, no irreplaceability; do as you’re told and you’re usable, and if you’re not convenient you’re swapped out, with zero retention value.
Stay as far away as you can — this role wears people down, has no future, and locks your technical path shut, purely wasting the golden years of a career.
But there’s one exception worth distinguishing: if this tech support role lets you deeply engage with customers’ real business scenarios, participate in solution design, or directly push product improvement with a real say — then it’s not pure consumption. But in reality, 90% of tech support has been sliced by KPIs into an assembly line of “take ticket - close ticket,” and for that kind, the sooner you see it clearly the better.
The Most Absurd Scene: A Grunt Job Gating Out 985 Grads and PhDs
This is the most absurd, most ironic part — it’s ridiculous enough to burst every pretense in the workplace.
A friend at Feishu told me that their tech support gates on a 985 background, and even PhDs go in as interns. A role that is plainly pure grunt work, repetitive labor, replaceability maxed out, a low-end consumable — yet it strictly gates on 985, master’s, and PhD thresholds.
The essence is just three hard truths:
First, it’s not that the role needs a degree — it’s that the company simply uses it to “screen people”
That bit of work in tech support — a junior-college grad can do it, a bachelor’s grad can do it, even someone with half a month of training can do it.
Requiring 985, requiring a PhD — it’s not that the difficulty of the work matches the degree; it’s that the company uses high degrees as a screening tool: screening for self-discipline, for obedience, for stress tolerance, for background. Use a high degree to filter everyone once, saving the cost of selecting people — plainly put, using degrees to pick high-grade consumables.
Second, labor is severely oversupplied, the rat race grinds degrees downward
There are too many smart people, too many highly educated people, and there are only so many roles, forcing 985 grads/PhDs to sink into doing tech support, this wedge role with no technical content. It’s not that the people don’t fit — it’s that there are too many people, high-end roles can’t hold them all, so they crowd into low-end roles, grinding the degree threshold for garbage roles up to the ceiling.
Third, the company wastes talent purely and raises its own stature
On one hand it has PhDs and 985 grads doing ticket-taking, Q&A, and ass-wiping trivia; on the other hand it flaunts outwardly “our team is high-degree and elite,” for appearances; internally it can also lower people’s mindset — even high-degree people come do this, so how dare you be picky about salary, how dare you disobey? Using the degree rat race, it turns around and pins everyone down.
Fourth, the most ironic closed loop
Work is just selling time for living expenses; promotion and growth are illusions; tech support itself is a garbage-consumable role; and because there are too many people, with an overflow of highly educated people, the company’s confidence is absurdly high — for grunt work, it dares to gate on 985, on PhDs, using talent as cheap, over-spec consumables at will.
Put plainly: it’s not that this role is valuable or has substance — it’s that the rat race has devalued people’s degrees and dragged highly educated people down into low-end, repetitive drudgery. Seeing through this, there’s even less reason to agonize over growth or platform — just treat it as grinding time for money, and don’t invest any expectations.
The Efficiency Revolution Brought by AI
Now let’s look at how AI overturns all of this.
Scenario One: OSS File Upload API
Just last week, I needed to integrate Alibaba Cloud’s OSS file upload feature.
The old workflow:
- Open the Alibaba Cloud official docs, first find the OSS SDK docs (5 minutes)
- Read through all the parameter descriptions, figure out the difference between bucket, endpoint, and accessKey (15 minutes)
- Copy the sample code into my project (10 minutes)
- Run it — error! Spend ages troubleshooting and find the region was set wrong (30 minutes)
- Error again! Turns out the signature expiration format was wrong (20 minutes)
- Can’t figure it out at all, open a ticket to Alibaba Cloud tech support and wait hours or even a day for a reply (N hours)
- Keep debugging back and forth…
Total time: half a day to a day
The workflow now:
- Open Cursor/Claude/ChatGPT and type: “I need to implement Alibaba Cloud OSS file upload in Python, give me the complete code”
- The AI gives the complete code directly, tells you where to apply for the accessKey, what to put for endpoint, what bucket is
- Copy the code, change the config, run it
- If there’s an error, just toss the error message at the AI and it tells you how to fix it
Total time: 5-10 minutes
Scenario Two: Auto-Renewing SSL Certificate Script
Another example: I needed to write a script to auto-renew Let’s Encrypt SSL certificates.
Before: study the certbot docs, figure out how the various plugins work, write a shell script, test it, debug it, hit a problem and dig through the docs again… couldn’t finish it in under half a day.
Now: just tell the AI “write a Python script to auto-renew Let’s Encrypt certificates, supporting wildcard certificates, using DNS validation, with Alibaba Cloud DNS,” and 3 minutes later the code is already running.
Scenario Three: AI-Automated Technical Documentation
This is a more subversive change. In the past, we spent a huge amount of time writing product docs, API docs, integration guides — and not only writing them, but maintaining and updating them to keep them consistent.
Now: AI can automatically generate documentation directly from the code, and it’s high quality.
An even more advanced play: feed these AI-generated docs to a large model for RAG (retrieval-augmented generation), then fine-tune it for specific scenarios. The final output is clearly structured, complete with examples, and updated in time — absolutely surpassing what a human writes.
In the past, how many pages of documentation could a technical writer produce in a day? Now AI generates dozens of pages in minutes, and can automatically sync with code changes. This gap in efficiency can no longer be described as an “improvement.”
A Qualitative Change in Efficiency
This isn’t a 10% or 20% efficiency gain — it’s a leap of dozens of times.
What used to take hours or even days of back-and-forth now takes minutes. And the quality is often higher — AI doesn’t make low-level mistakes, doesn’t miss parameters, doesn’t write code with security holes (as long as you ask in enough detail).
Where’s the Value of the Tech Support Role?
When AI can easily handle 90% of “simple inquiries” and “API integration problems,” what core value is left in traditional tech support?
The answer: it becomes more and more marginalized. That’s exactly the state of things before my layoff.
If your daily work is:
- Answering customers’ “how do I use this API”
- Helping customers troubleshoot “why is it returning a 403”
- Writing some simple scripts and processing logic
Then your role being replaced by AI is only a matter of time. Not this year, then next year. If you’re still there, it only means you’re cheap enough — but you’re still not as cheap as AI, and being laid off is just a matter of when.
This Isn’t Doomsday, It’s a Turning Point
But I’m not writing this article to sell anxiety. On the contrary, I think this is a huge opportunity.
The Old Model Is Destined to Die Out
Let’s face it honestly: the traditional tech support working model has plenty of problems to begin with.
- Highly repetitive, limited room to grow
- Passive response, lacking a sense of accomplishment
- Treated as a “cost center” rather than a “value center”
- Easy to quantify, easy to replace
Rather than holding on hard on a ship that’s destined to sink, better to jump off early and learn to swim.
What Abilities Does the New Era Need?
AI isn’t trying to eliminate the need for “tech support” — it’s trying to redefine the boundaries of “tech support.”
Future tech support (or rather, technical enablement) should have the following abilities:
1. Abstracting and breaking down complex problems
AI is good at handling clear-cut problems, but fuzzy, cross-system problems requiring deep business understanding still need a person. The key is whether you can break a fuzzy requirement down into concrete steps AI can execute.
2. System design and architecture ability
Simple API integration? AI can do it. But designing a system that’s highly available, scalable, and accounts for all sorts of exceptional cases? That requires human experience and judgment.
3. Product thinking
Rather than answering “how do I use this” every day, think about “why would the user ask this,” then push for product improvement so the problem never arises at all.
4. The ability to collaborate with AI
Knowing how to ask AI questions, how to judge whether the AI’s proposed solution is reasonable, and how to integrate AI output into actual projects. This is the core skill of the new era.
My Choice
So when I got the layoff notice, I wasn’t too upset.
There’s a reason for that. I started doing tech support in 2015. Over a dozen years I changed employers three times; my salary went from 5,000 to 10,000 and finally to a bit over 25,000 at my last job — it took a full dozen-plus years to climb to that number. Meanwhile some technical roles pay over 20,000 right out of school. The ceiling of tech support is just that low — absurdly low.
More realistically, after being laid off, when I look for related work again, the offered price is only 15,000 or even lower. A dozen-plus years of accumulation not only doesn’t appreciate in the market — it depreciates. A career path with zero cost-performance; continuing to grind it out has no meaning at all.
I took the severance and signed and left without much hesitation (though I wrestled over the amount for a while), but I was definitely going to leave, and I had no anxiety at all.
Because I know:
- That role was on a downward slope to begin with — staying would only delay being laid off.
- Technological enablement in the AI era is a completely different game — the earlier I embrace it, the sooner I build a new moat of ability.
- Time is the biggest opportunity cost — rather than consuming it in the old model, better to invest it in learning new skills.
Advice for Friends Still in Tech Support Roles
If you’re still in a traditional tech support role, I have a few pieces of advice:
1. Assess how much of your work can be replaced by AI
Make a list and see, of the things you do every day, which are simple lookups, integrations, and Q&A. If more than 50% is that kind of work, you need to be on alert.
2. Start deliberately practicing “collaborating with AI”
Don’t just treat AI as a replacement for a search engine — really use it to get work done. Writing code, writing docs, analyzing problems — try using AI to assist with all of it; you’ll find the efficiency gain is astonishing.
3. Extend upstream and downstream
- Upstream: learn product thinking, participate in product design, reduce problems at the source.
- Downstream: dive into technical details, understand architecture design, become someone who can solve complex problems.
4. Build your own work and influence
In the AI era, a personal brand matters more than a company title. Write articles, do open source, share experience, let more people know what you can do.
5. Change roles and do something else as soon as possible
This is the most important point, and my most heartfelt words: if you can no longer see a ceiling in a tech support role, then don’t hesitate — change roles as soon as possible.
Don’t wait until you’re laid off to think passively about the next step. With this role, the longer you do it, the higher the cost of transitioning; the longer the “tech support” label sticks to your résumé, the less room you have to negotiate when you jump ship.
If you can move to R&D, move to R&D; if you can move to product, move to product; if you can move to presales, move to presales — even moving to ops or implementation beats grinding away in the dead end of tech support. The key is to escape the “take ticket - close ticket” loop as early as possible, and enter a role where you can accumulate core abilities, have pricing power, and have an experience premium.
There’s a vivid contrast right next to me. A colleague at my first company started in tech support from his internship, but he decisively jumped ship after 3 years to do R&D at Tesla, with a salary starting at 40-50k. Also from a tech support background, he made the switch early and his path was completely different.
The earlier you switch, the lower the cost, the more room for a premium.
Of course, I also have to honestly say why I myself didn’t switch earlier.
First, the degree gate is a hard wound. The threshold for many R&D and product roles isn’t ability, it’s the degree — and that single gate blocks off more than half the road.
Second, my age is also up there. Once you reach a certain age, walking the job-switching path of being employed is basically a lost cause. A company would rather have a fresh graduate with low salary demands who can still work overtime than a “high-age career changer.” That’s reality, not pessimism.
So the core point of this article isn’t to teach everyone “how to pivot gracefully from tech support,” but rather: if you’re still young and still have choices, never take the road I took. Time is your most precious asset; don’t waste it on something destined to depreciate.
In Closing
The old era of tech support really is ending, but the new era of technical enablement has only just begun.
AI isn’t here to steal your bowl — it’s here to redefine what counts as “valuable technical work.”
Those repetitive, mechanical, low-value-added jobs should have been automated to begin with. And we humans should put our energy into fields that are more creative, that require more judgment, and that generate more value.
Getting laid off isn’t the end. Saying goodbye to the old era and embracing the new paradigm — that’s the real beginning.
If you’re also interested in career transition in the AI era, feel free to share your thoughts in the comments.
评论
0 条评论