I remember the first time I proposed hiring a Technical Support Engineer.

It was 2019. There were three of us in support, and none of us were technical. Any issue that needed even a basic database change meant an automatic escalation to Engineering. 

Every time it happened, we paid for it twice. Engineering lost hours to context-switching. Customers lost days waiting for the fix.

My team pushed back constructively. "Do we actually need a support engineer? Why not just hire 'technical support'?"

My answer was simple: we needed someone who could own technical complexity inside support. Someone who could save engineering time, reduce time to resolution, handle the messy edge cases without turning every ticket into a handoff, and earn credibility with engineering rather than borrow it.

The title mattered. It wasn't about inflation. It was about signal.

So we hired our first Technical Support Engineer. Within months, we had internal tools we didn't have before: a log scraper, an account debugger. During incidents, support engineers owned troubleshooting instead of relaying messages. Customers got real technical diagnosis, not scripted escalations. Engineering got hours back. 

That was almost 7 years ago. Since then, I've watched this role quietly prove itself at company after company.

And now, in 2026, it's not quiet anymore. Support is hitting an inflection point, and the teams that understand what's changing are going to pull away.

Why Now

This isn't a gentle evolution. It's a moment where the middle is getting squeezed, and two very different paths are emerging.

To see why, look at what AI is doing to support work right now.

Klarna's AI assistant reportedly handled 2.3 million customer conversations in its first month. Resolution times dropped from 11 minutes to under 2 minutes. The company said it was doing the work of 700 full-time agents. That's the category of work AI is taking first: password resets, order tracking, "where do I find this setting?" Repetitive, high-volume, low-context questions.

We all knew this work was automatable. We just didn't have the technology to do it well.

The important part isn't "AI is replacing humans." The important part is what happens next.

Klarna also started hiring humans again. Not because the AI failed, but because the all-AI approach started optimizing for cost over quality. AI scaled the floor; Klarna is now reinvesting in the ceiling. The complex cases, the emotional moments, the situations requiring judgment: those still needed humans. Better humans. More skilled humans.

That's the pattern everywhere now. AI absorbs the volume, humans inherit what's left, and what's left gets harder every quarter.

Because the tickets that survive AI triage aren't "how do I reset my password?" They're: "Your API is returning a 429 on the third retry, but only when we're using exponential backoff with a 500ms jitter. What's happening?"

That isn't customer service. That's a technical investigation. Different work. Different expectations. Different skill set.

Two Futures for Support Work

Here's the simplest way I can put it:

Path 1: Service Gets Automated

A large chunk of traditional support work becomes AI-first by default, with humans stepping in for exceptions, judgment calls, and emotionally charged moments. There will still be jobs here, but the work will keep shifting toward relationship repair, decision-making under ambiguity, policy and risk judgment, and "this is complicated and the customer is upset" moments.

The Bureau of Labor Statistics projects customer service representative roles will decline 5% over the next decade, though they also project roughly 340,000 openings per year due to turnover. The work isn't disappearing entirely. It's transforming.

Path 2: Investigation Becomes the Value

The work that can't be automated moves closer to the codebase. This is where support engineers live.

This path is growing because it's tied to product reliability, incident response, developer experience, customer trust in technical moments, and the speed at which your company can learn from real-world failures.

AI doesn't kill this work. AI makes it more visible, because it removes everything around it.

Enter the Support Engineer

Support engineers sit in the middle. Between the customer and the codebase. Between the support queue and the engineering sprint. Between "here's what the customer said" and "here's what's actually happening."

They can investigate a complex issue without escalating immediately. They can translate a messy, frustrated report into something clean and actionable. And they make engineering faster, not by asking for help better, but by doing more work before the handoff.

When a support engineer escalates, it comes with logs, reproduction steps, what they've ruled out, and a hypothesis worth testing. "Here's the edge case. Here's why I think it's happening. Here's what I tried."

That credibility changes everything. It's the difference between a ticket sitting in a backlog for weeks and an engineer saying, "OK. I see it. I'll look today."

What This Means for You

Let's talk about the part people usually skip.

Not everyone will become a support engineer. Some people won't want to. Some people won't have the time or organizational support. Some people will be in companies that don't invest in pathways. That's real. And pretending otherwise makes this kind of article feel disconnected from reality.

But here's the hopeful part: most great support people already have the foundation. Problem-solving, pattern recognition, communication under pressure, calm with customers, strong judgment, context-stitching across teams.

The gap usually isn't talent. It's technical literacy. And technical literacy is learnable.

If You're an Individual Contributor

You don't need to become a software engineer. You need to become dangerous enough to investigate, and credible enough to be trusted.

Start by learning to read what your system is already telling you: logs, request IDs, error messages, status codes, patterns. Get comfortable with APIs. If your product touches one, learn how requests work, what 401 vs 403 vs 429 means, and how to test a request. Volunteer for the messy tickets. Not the angry ones, but the ones that require investigation instead of process.

And one small but powerful move: write escalations like an engineer will read them. Repro steps, evidence, what's ruled out, and a clear question. Build a habit of writing the "clean summary" before you escalate, even if nobody asked. That skill alone will differentiate you.

If You're a Support Leader

If you want to keep your best people, you need pathways, not pep talks.

A simple model that works: pick one or two people who want the track, give them protected learning time (non-negotiable), partner with engineering for a rotation or buddy system, define what "good investigation" looks like, and celebrate the boring wins. Reduced escalations, faster triage, cleaner bugs.

Make the role explicit. Support engineers aren't "support but with more pressure." They're a specific bridge function. Define the contract and everyone gets faster.

If You're a Company

Ask honestly: Which parts of support are AI-enhanced (humans plus tools)? Which parts are AI-vulnerable (already automatable)? Which parts are AI-resistant (judgment plus technical investigation plus context)?

Then look at your org chart and ask: Do we have the bridge role? If not, are we expecting Engineering to do it? Or are we quietly letting customers wait?

Not every company needs a full support engineering function. But every company needs someone who can turn technical customer pain into fast truth.

The best support orgs won't be the ones with the most automation. They'll be the ones who use automation to fund higher-skill work.

2026: The Year of Choice

Let me be clear about what I'm not saying.

I'm not saying every support professional needs to learn to code. I'm not saying every company needs a full support engineering team.

I am saying this: technical literacy is becoming table stakes. The ability to read an API error message, navigate developer docs without fear, understand how systems connect, talk to engineering teams in substance rather than vibes, and investigate before escalating. That is quickly moving from "nice to have" to "expected."

This isn't about being AI-proof. It's about being AI-resistant: moving into the category of work where judgment, context, and technical understanding still matter.

I didn't know it at the time, but that first support engineer hire in 2019 was a bet on this moment.

A bet that the most valuable support work would move closer to the product. A bet that the people who can bridge customers and code wouldn't just be helpful. They'd be essential.

In 2026, that bet isn't a niche strategy anymore. It's a choice.

For ICs: build literacy. For leaders: build pathways. For companies: build bridges, or accept slower learning, slower fixes, and slower trust.

Because the future of support won't be decided by who answers tickets fastest. It'll be decided by who can turn real-world customer pain into technical truth, while everyone else is still forwarding the message.