Randomness is the only constant when it comes to network outages. It’s past midnight, and you’re on a bridge call with people from companies that support your network. Customer traffic is degraded, and monitoring is off by margins. The upstream provider’s NOC engineer has been on mute for minutes, and when he comes back, he says the line every network engineer has heard a hundred times: “We’re not seeing anything on our side.”
He’s wrong. I can see his AS (autonomous system) in the path of routes that just went unstable, and the timing lines up exactly with a maintenance window his team quietly extended. But knowing he’s wrong and getting him to act on it are two different problems. Only one of them is technical.
I spend my days building the kind of AI system that is supposed to replace this job. I lead a telemetry and anomaly detection platform that processes more than 100 million data points a day across more than 100,000 network devices. It works. And it is not coming for the part of my job that actually matters.
Does AI Threaten Network Engineers?
While AI can handle pattern recognition, log triage and config generation, it cannot replace human network engineers. Outages are ultimately resolved through deep domain expertise, human negotiation, and clear incident communication. These are all coordination skills that AI models cannot replicate.
What Is AI Good At in Network Operations?
The counter-narrative to AI hype shouldn’t be denial. Modern machine learning is genuinely good at the parts of network operations that look like pattern recognition over structured data. Anomaly detection on interface counters, BGP session metrics and latency distributions is squarely in the wheelhouse of statistical models, and it works at scales no human can match.
Large language models are also surprisingly capable at the next layer up. Alert correlation, runbook suggestions, log triage and the first-pass narrative of “Here’s what’s probably happening” are all things a capable model can do well. Configuration generation is real, too. I can describe an access control list change in plain English and get syntactically correct output for half a dozen vendor dialects in seconds.
If your day is mostly staring at dashboards, correlating alerts, drafting configs or transcribing tribal knowledge into runbooks, your job is changing.
The Hardest Part of an Outage Isn’t Diagnosis
Every serious incident I’ve worked on has the same shape. Detection is fast. Diagnosis is mid-range. Mitigation is bounded. Resolution is slow and is almost always blocked by something other than understanding.
That blocker might be a vendor whose escalation path you only know because you’ve burned through it three times before. Or an internal team that owns the dependency you need changed. Or an executive who needs to be told, in 90 seconds, what is broken and what to tell the customer. Or a peering partner who has both a contractual advantage and reputational pride and won’t move until you give them a way to save face.
These are coordination problems wearing technical clothing. AI can draft the executive update. It can’t read the room and know that this VP needs the customer impact, not the root cause. AI can tell you the upstream’s AS is in the path. It can’t detect that the NOC engineer is stalling because his shift lead told him not to admit anything until legal weighs in.
If you’re a network engineer today or thinking about becoming one, the skills that solve those problems are the most important career proficiencies you’re going to get this decade.
Develop Deep Domain Expertise
BGP and inter-domain routing is mine. Optical is another. Hardware-level forwarding behavior, vendor-specific know-how, peering politics and submarine cable operations are domains where the right answer requires knowing things that are not on the public internet for a model to train on. RFC 4271 is freely available, and any model can recite the BGP state machine on demand.
But the operational reality of BGP, where transit provider routinely deprioritizes your prefixes during congestion, or pre-emptively build a new peering session with a peer given the increase potential in traffic, lives in the heads of the few hundred people who have been running large networks for a decade. A model can ingest the documentation. It cannot ingest a relationship or a handshake deal.
Pick one. Go deep. Be the person whose pattern-matching across the last 10 incidents is more valuable than any tool. The standard advice to early-career engineers is to learn automation and the cloud. I agree with that, but if it is the only thing you optimize for, you’re training to compete head-on with the technology that is improving fastest. That is a bad bet.
Deep domain expertise moves in the opposite direction — it compounds. Every incident you work in your chosen domain adds to a pattern library no model has access to because most of what you learn never gets written down in a form anything could train on. After a decade in BGP, you know which symptoms correlate with which classes of upstream failure, which vendor combinations produce specific edge-case behaviors, which peers are honest brokers and which ones bluff. That accumulated context is what makes the tools more valuable in your hands than in anyone else's.
Treat Incident Communication As a Learnable Skill
Most engineers treat communication skills as something you either have or you don’t. That’s wrong. Writing a clear executive update under pressure, structuring a postmortem so it actually drives change and running a bridge call so the right decisions get made are all learnable.
Good incident communication is, first, the ability to write three sentences for an executive update, eight sentences for a peer engineer and a one-line Slack post for the on-call channel — all describing the same incident, all calibrated to what each audience can actually do with the information. The executive needs customer impact, current mitigation and time to resolution. The peer engineer needs the technical hypothesis and what you’ve ruled out. The on-call channel needs the next concrete action and who owns it.
Second, it’s the ability to ask questions on a bridge call that move the call forward. “Are you seeing anything on your side?” is a dead question; the answer is almost always no. “Can you confirm whether session X to peer Y was reset between 02:47 and 02:53 UTC?” is a live question. It gives the other side something specific to look at, it timestamps the request and it makes future denial harder.
Third, it’s the ability to write a postmortem that actually changes the system. A good postmortem isn’t a chronology; it’s a structured argument about what failed in the larger socio-technical system, with concrete action items each owned by a named person with a date. A bad postmortem reads like a confession and produces nothing. The difference is teachable.
Volunteer to be the incident resolver you could otherwise just observe. Ask a senior engineer to review your status updates. Read postmortems from other companies and notice what makes the good ones good. This compounds faster than almost any technical skill, and it’s far less likely to get automated.
Use AI As a Force Multiplier
The engineers who will do best in the next decade aren’t the ones who refuse AI tools. They’re also not the ones who outsource their entire judgment to them. Instead, they’re the ones who treat a capable model the way a senior engineer treats a strong junior: delegate the legwork, verify the output and reserve their own attention for the parts that require taste, context and accountability.
If you can do alert triage, config drafting and runbook synthesis in a fraction of the time it took five years ago, you don’t have less to do. You have the bandwidth to do the things that were always more valuable but never had room in the day. Things like reducing your team’s tech debt by producing docs to understand your complex network which did not exist earlier. Or creating read.me for code repos that did not exist earlier. Or making design docs to understand tech debts and propose solutions for complex code bases. Or pairing with a junior engineer on a real incident response and walking them through your decision-making in real time. Or doing the capacity planning analysis that nobody assigns but everyone needs. None of these show up on a sprint board, and AI doesn’t replace them — it creates the room to do them.
Will AI Replace Network Engineers?
No. But the shape of the job is changing, and the engineers who optimize only for the parts that AI is getting better at will see their advantages erode.
The hardest part of an outage was never the diagnosis. It was the bridge call, the negotiation, the judgment call and the relationship that made the negotiation possible in the first place. That part isn’t getting easier. It’s getting more valuable because AI is finding more incidents faster and the humans available to handle them are not multiplying.
If you’re a network engineer, build for that. If you’re entering the field, build for that from day one.
