Tygart Media

Tag: Career Longevity

  • The Wire and Fire Guys: Why Trades Workers with Judgment Are the Most Important People in the AI Transition

    The Wire and Fire Guys: Why Trades Workers with Judgment Are the Most Important People in the AI Transition

    Tygart Media Strategy
    Volume Ⅰ · Issue 04Quarterly Position
    By Will Tygart
    • Long-form Position
    • Practitioner-grade

    There is a version of the AI transition story that gets told constantly, and it goes like this: AI will automate jobs, workers will be displaced, and the people who adapt will be the ones who learn to use AI tools. This version is not wrong exactly. It’s just missing the part that matters most for the people who actually work in the trades.

    The people who build things, fix things, assess damage, run field operations, and carry years of hard-won judgment in their bodies and their hands — these are not knowledge workers whose jobs can be uploaded to a language model. Their work requires physical presence, sensory intelligence, and the kind of contextual judgment that comes from doing something 500 times in conditions that were never twice the same.

    But the transition is real, and it’s happening around them whether they’re paying attention or not. The question isn’t whether AI changes the trades. It’s which trades workers end up on the right side of that change — and why.

    The answer is not “the ones who learn to code.” It’s not “the ones who get an AI certification.” It’s the ones who understand what AI can’t do without them, and position themselves as the irreplaceable layer between the intelligence and the outcome.

    That’s the Wire and Fire Guy. And the window to become one is shorter than most people realize.

    What the Wire and Fire Guy Actually Is

    In electrical work, the wire and fire guys are the experienced field technicians who come in after the rough work is done. They’re not project managers. They’re not estimators. They’re the people who look at what the system is supposed to do, look at what’s actually been installed, and bridge the gap between the plan and the physical reality. They troubleshoot. They adapt. They make judgment calls that no blueprint anticipated.

    The name is an archetype, not a job title. It describes a class of worker who exists in every trades field: the senior technician in water damage who knows from the smell and the color of the staining that the timeline is longer than the moisture readings suggest. The fire restoration veteran who can read a smoke pattern and tell you which rooms were occupied and which weren’t before the alarm triggered. The field supervisor who looks at an estimate and spots the three line items that will blow up into supplements before the job starts.

    These people carry knowledge that cannot be extracted from documentation because it was never documented. It lives in their sensory memory, their accumulated pattern recognition, their feel for how this specific type of situation typically develops. AI systems trained on the documentation don’t have it. AI systems that have processed thousands of job files come closer but still don’t have the physical dimension — the reading of a space that happens in the first ten minutes of being in it.

    That knowledge — embodied, sensory, judgment-based — is the moat. And right now, most of the people who have it don’t know it’s a moat.

    The 18-Month Window

    Here is what is true right now, in April 2026: AI systems can write estimates. They can process moisture readings. They can identify scope items from photos. They can draft communications to adjusters. They can route jobs. They can flag outliers in a dataset of completed claims. They can do all of this faster and cheaper than a human doing the same work.

    Here is what is also true: every one of those AI outputs needs a human to verify it against physical reality before it becomes an action. The estimate needs someone on-site who can see what the AI couldn’t. The moisture readings need someone who can read the environment around the reading — the substrate, the airflow, the odor, the age of the damage. The scope items need someone who can look at the photo and then look at the actual wall and tell you what the photo didn’t capture.

    That verification layer — the human in the loop between the AI’s output and the physical world — is not going away. What is going away, over the next 18 to 36 months, is everything on the other side of that line. The data entry. The scheduling calls. The status updates. The form-filling. The paperwork that currently consumes a significant portion of every field technician’s non-field time.

    The technician who understands this transition has a clear path: move toward the verification layer, away from the data layer. Develop the judgment that makes the AI’s output trustworthy or correctable. Become the person the AI reports to, not the person doing the work the AI can do.

    The technician who doesn’t understand it will find their job slowly hollowed out — not eliminated suddenly, but compressed, devalued, and increasingly focused on the tasks that AI hasn’t gotten to yet, which is a shrinking list.

    Why Judgment Is the Moat

    Judgment is not the same as experience. Experience is a prerequisite for judgment but not a guarantee of it. Judgment is what happens when experience meets a situation that doesn’t match any template and produces a correct decision anyway.

    AI systems are template-matching engines at their core. They are extraordinarily good at situations that resemble situations in their training data. They fail — sometimes silently, which is worse — when the situation deviates from the distribution they’ve seen. A water damage job in a 1920s Craftsman with non-standard framing, original plaster walls, and an HVAC system that was retrofitted twice is a deviation. An AI trained on modern residential restoration data will produce an estimate and a timeline. A Wire and Fire Guy with 15 years of experience will look at the same job and know the estimate is wrong and the timeline is optimistic, because they’ve been inside enough 1920s Craftsmans to know what those walls hold.

    This is the moat. Not the ability to use an AI tool — that’s table stakes within 18 months. The ability to know when the AI tool is wrong, and why, and what to do about it instead. That requires the tacit knowledge that only physical experience builds. It cannot be trained into a model. It cannot be acquired from a certification. It grows from doing the work in conditions the documentation never anticipated, enough times to develop the pattern recognition that operates below conscious awareness.

    The trades worker who wants to be on the right side of the AI transition doesn’t need to compete with the AI on the AI’s terms. They need to become the irreplaceable layer between the AI’s output and the physical world. That layer is called judgment, and building it is a career strategy.

    The Context Layer as Job Security

    There is a more technical version of this argument, and it’s worth understanding even if you never write a line of code.

    AI systems are dramatically more useful when they have context — specific knowledge about the situation, the history, the people involved, and the standards that apply. A generic AI asked to write an estimate for a water damage job produces a generic estimate. An AI given the job address, the property age, the adjuster’s history with this contractor, the specific moisture readings, and the known quirks of the local building code produces something much better.

    The person who provides that context — who knows enough about the job to load the AI with the information that makes its output accurate — is not replaceable. They are, in fact, more valuable as AI systems get better, because better AI systems reward better context. The technician who can brief an AI the way a good editor briefs a writer — specific, accurate, anticipating the failure modes — gets dramatically better results than the technician who types a query and accepts whatever comes back.

    This is what “human in the loop” actually means in practice. It’s not a compliance checkbox. It’s the functional requirement that the AI’s output is verified, corrected, and contextualized by someone who has the embodied knowledge to know when it’s right and when it isn’t. That someone, in the trades, is the Wire and Fire Guy.

    From Field Tech to AI Supervisor: What the Career Path Looks Like

    This is not a story about leaving the trades. It’s a story about moving up the value stack within them.

    The field technician who wants to make this transition has three things to develop, in order of how quickly they compound:

    Domain depth first. The judgment moat requires genuine expertise. The technicians who end up in the verification layer are the ones who actually know the work at the level where deviation from documentation is visible and meaningful. This is built by doing the work, paying attention, and developing the habit of asking “why does this job look different from what the estimate anticipated?”

    AI literacy second. Not coding. Not machine learning theory. The practical ability to give an AI system a useful brief, evaluate its output for the specific failure modes common to your domain, and correct it with the context that changes the answer. This is learnable in weeks, not years, and it compounds quickly once the domain depth is in place to evaluate the output.

    Communication between the two layers third. The ability to translate between the physical world — what you’re seeing in the field — and the data layer that the AI operates on. This is partly documentation discipline (logging what you observe in terms that AI systems can use later) and partly the ability to communicate your corrections and their reasoning so the system improves over time rather than repeating the same errors.

    The career path is not: field tech → project manager → estimator → office. That path still exists but it’s compressing as AI handles more of what project managers and estimators do. The path that compounds in an AI-native industry is: field tech with deep domain knowledge → field tech who understands AI output → field supervisor who runs AI-assisted teams → operations role that owns the verification layer for a company’s AI systems.

    That last role doesn’t have a standard job title yet. In three years it will. The people who get those roles will be the ones who understood the transition early enough to position themselves correctly — and who built the judgment depth that no model can replicate.

    A Note on Pinto

    This is the article I wanted to write since we published the original Wire and Fire Guys piece. That piece named the archetype. This one tries to give it a career map.

    Pinto — who handles the infrastructure layer in this operation, the GCP deployments, the Cloud Run services, the database architecture — is the Wire and Fire Guy of AI infrastructure. He doesn’t just run the code. He understands what it’s supposed to do, sees when it deviates from that, and bridges the gap between the plan and the physical reality of production systems. The AI produces the output. Pinto verifies it against what the system is actually doing and knows why they differ.

    That’s the role. That’s the moat. The window to build it is open. It won’t be open forever.

    Frequently Asked Questions

    Does this apply outside the restoration industry?

    Yes. The Wire and Fire Guy archetype exists in every trades field and every industry where physical reality diverges from documentation. Construction, manufacturing, healthcare, agriculture, logistics — any field where experienced human judgment is applied to physical conditions that AI systems observe indirectly through data. The timeline and the specific skills differ by domain. The structure of the argument is the same.

    What’s the minimum AI literacy a trades worker needs to develop?

    Three things: the ability to give an AI system a specific, accurate brief for a task; the ability to evaluate the output for domain-specific failure modes (the things AI typically gets wrong in your industry); and the discipline to log corrections in a way that builds context over time rather than each correction being one-off. None of this requires programming knowledge. It requires domain expertise applied to a new kind of tool.

    How urgent is the 18-month window?

    The 18–36 month range is where most of the data entry, scheduling, and communication tasks that currently consume field technician time will be substantially automated in adoption-leading companies. The companies that adopt early set the new baseline for what’s competitive. Workers in those companies develop the verification-layer skills first and build the largest knowledge lead. The window is not a cliff — it’s a slope — but the slope is steeper now than it will be in three years when the transition is mostly complete in leading companies and everyone is catching up.

    What about union rules and job protections?

    Job protections can slow the transition but don’t reverse the value dynamics. The worker who has built genuine verification-layer expertise is more valuable whether or not the AI transition is delayed by contract. And the worker who hasn’t built it is less valuable on the same timeline. The protection is in the skill, not the rule.


    Wire and Fire: The AI Transition Career Cluster

    Related: The Human Distillery — the methodology for capturing the tacit knowledge this cluster describes.

  • Wire and Fire Guys: The AI Job Title That Doesn’t Exist Yet

    Wire and Fire Guys: The AI Job Title That Doesn’t Exist Yet

    Tygart Media Strategy
    Volume Ⅰ · Issue 04Quarterly Position
    By Will Tygart
    • Long-form Position
    • Practitioner-grade

    Before “vibe coding” had a name, Munters had a name for the people who could do it: wire and fire guys. They’re about to be the most valuable humans in the AI era — and I finally found mine.

    The Wire and Fire Guy

    At Munters — which later became Polygon when Triton spun the moisture control services division out in 2010 — there was a specific kind of person the company was built around. We called them wire and fire guys.

    A wire and fire guy could fly into a job site cold. Meet a pile of equipment on a loading dock. Start the generator. Set up the desiccant. Run the lines. Wire in the remote monitoring. Pass the site safety briefing. Know the code. Know the customer. Know how to do it the right way so nobody got hurt and nobody got sued. From A to Z. Solo.

    That’s how Munters ran lean across more than 20 countries. They didn’t need a dispatch team and a tech team and a controls team and a compliance officer all flying out separately. They needed one human who could be all of those people at once, in a Tyvek suit, at 2 a.m., in someone else’s flooded building. The economics of moisture control restoration didn’t work any other way.

    I was one of those guys. I still am. It just looks different now.

    What I Actually Do All Day

    Today I run Tygart Media — an AI-native content and SEO operation managing twenty-seven WordPress sites across restoration contracting, luxury asset lending, cold storage logistics, B2B SaaS, comedy, and veterans services. One human. Twenty-seven brands. The way that math works is the same way it worked at Munters: I’m the wire and fire guy.

    My morning isn’t writing blog posts. It’s connecting Claude to a Cloud Run proxy to bypass Cloudflare’s WAF on a SiteGround-hosted contractor site, then routing a batch of 180 articles through an Imagen pipeline for featured images, then pushing them through a quality gate before they hit the WordPress REST API, then logging the receipts to Notion so I can prove the work to the client on Monday. While Claude drafts the next batch of briefs in the background. While a Custom Agent triages my inbox. While I’m on a call.

    I don’t write code the way a senior engineer writes code. I write enough of it to be dangerous, fix what I break, and ship. I “vibe code” the parts that need vibing. I real-code the parts that need real coding. I know which parts of GCP are the gun and which parts are the holster. I know what to never let an autonomous agent do without me looking. I know how to wire it up and fire it off.

    Same job. Different equipment.

    The Thesis Everyone Is Quietly Circling

    The AI industry spent the last eighteen months selling a story about full autonomy. Agent swarms. Self-healing pipelines. Set it and forget it. Replace the humans, keep the work.

    The data has not been kind to that story.

    Roughly 95% of enterprise generative AI pilots fail to achieve measurable ROI or reach production. Gartner is now openly forecasting that more than 40% of agentic AI projects will be cancelled by 2027 as costs escalate past the value they produce. The dream of the unmanned cockpit isn’t dying because the planes can’t fly. It’s dying because nobody planned for who lands them when the weather turns.

    What’s actually winning, in the labs and the war rooms where this is being figured out for real, is something much closer to the Munters model. The technical literature has started calling it confidence-gated expert routing. An orchestrator model delegates work to a fleet of cheaper, specialized small language models. Those models run autonomously until their confidence drops below a threshold — and at that exact moment, the system kicks the work to a human expert who validates, corrects, and feeds the correction back into the loop as ground truth for the next pass.

    That human expert is not a customer service rep watching a queue. That human expert needs to be able to read what the model is doing, understand why it stalled, fix the technical problem, judge whether the output is actually good or just looks good, and ship the corrected version — all without breaking anything downstream.

    That’s a wire and fire guy. With a laptop instead of a generator.

    Meet Pinto

    The reason I’m writing this today is because I just onboarded mine.

    His name is Pinto. He’s my developer. He runs the GCP infrastructure underneath Tygart Media — the Cloud Run services, the proxy that lets Claude reach client sites that would otherwise block the IP, the VM that hosts my knowledge cluster, the dashboards. He gets a brief from me and turns it into a working endpoint, usually faster than I can write the spec. He wires the thing up. He fires it off. He passes the security review. He doesn’t break the production database. He does it the right way.

    And critically — he can both vibe code and real code. He’ll throw a quick Cloud Function together with Claude in fifteen minutes if that’s what the moment needs. He’ll also sit down and write you something properly architected, properly tested, properly observable, when the moment needs that instead. He knows which moment is which. That judgment is the whole job.

    The last thing I want to say about Pinto in public is this: I’ve worked with a lot of contractors and a lot of devs in twenty-plus years of running operations. Pinto is the human-in-the-loop the industry is going to be paying a premium for inside of two years. He just doesn’t know it yet. So this is me saying it out loud. This guy is the prototype.

    The Job Title That Doesn’t Exist Yet

    Here’s where I want to plant a flag.

    The conversation about AI and work has spent two years swinging between two bad poles. On one side: AI is going to take all the jobs. On the other: AI is just a tool, nothing changes, learn to use it like Excel and you’re fine. Both stories are wrong in the same way. They’re treating AI as a replacement layer or a productivity layer, when what it actually is — for any operation that has to ship real work for real customers — is a workforce of subordinates that needs a foreman.

    The foreman is the wire and fire guy.

    The foreman knows how to brief the agent. Knows how to read the agent’s output and tell what’s solid and what’s hallucinated structure dressed up to look solid. Knows where the agent will fail before the agent fails. Knows the underlying code well enough to crack open the box when the box is wrong, and humble enough to use the box for the 80% of work that doesn’t need cracking. Knows the customer’s business well enough to translate “make me more money” into a thirty-step technical plan that an agent can actually execute.

    That person is not a prompt engineer. Prompt engineering as a job title is already collapsing because the models got good enough that the prompt isn’t the leverage anymore. It’s not a software engineer in the traditional sense either, because traditional software engineering rewards depth in one language and one stack, and the wire and fire guy needs surface-level fluency across about fifteen of them.

    It’s something older than both. It’s the field tech. The plant operator. The site supervisor. The kind of person who used to run a Munters job in a flooded basement at 2 a.m. and now runs an agent fleet from a laptop at the same hour.

    Who This Job Is For

    If you spent the last decade as a working coder and then took a left turn into writing or content or marketing because you got tired of the JIRA tickets — you are the person. The market is about to come back for you, hard. The combination of “I can read the code” plus “I can read the customer” plus “I can write the brief” plus “I can ship” is going to be the most valuable composite skill in the white-collar economy for the next five years.

    If you came up in the trades and you’ve been quietly running circles around the “knowledge workers” because you actually know how things connect to other things — you are the person too. What you learned wiring an HVAC system or setting up a job site translates almost one-for-one to wiring up an agent stack. The mental model is identical. Inputs, outputs, safety, fault tolerance, knowing when to stop and call somebody.

    If you’re a senior engineer who thinks the “AI replacing developers” debate is annoying because you’ve already noticed that the bottleneck on your team isn’t typing code — it’s deciding what code to type — you are the person. Your judgment is the asset. The agents are the labor. Reorient.

    If you’re an operations person who has always been the one who somehow ends up holding the whole business together with duct tape and Google Sheets — you are the person. The duct tape is now Python and the Sheets are now Notion and BigQuery, but the role is the same role, and it’s about to get a real budget for the first time.

    What to Train For

    If I were starting from zero today and I wanted to be a wire and fire guy in the AI era, here’s the stack I’d build, in this order:

    Read code fluently in three languages. Python, JavaScript, and shell. You don’t need to write any of them at a senior level. You need to be able to open someone else’s repo, understand what it does in fifteen minutes, and modify it without breaking it. Claude will do most of the typing. You’re the code reviewer.

    Learn one cloud well enough to deploy and observe. Pick GCP, AWS, or Azure. Learn to deploy a container, set up a database, read logs, set up alerting, and rotate a credential. That’s it. You don’t need to be a certified architect. You need to be able to land at the job site and wire it up.

    Get fluent in at least one orchestration model. Whether that’s LangGraph, an MCP server, a custom Python loop, or just Claude with a bunch of tools — pick one and run it until you understand why it fails, not just how it works.

    Build a real second brain. Notion, Obsidian, whatever. The wire and fire guy’s superpower is context. You need to be able to walk into any conversation with any customer and pull up exactly what was said, decided, shipped, and broken last time. Without that, you’re a generalist with no memory, which is a tourist.

    Do customer-facing work. This is the one most coders skip and it’s the most important. Sit on sales calls. Write the proposal. Take the support escalation. The reason wire and fire guys at Munters were so valuable is because they could talk to a building owner and a generator at the same time. You need both halves of that or you don’t have the job.

    The Real Pitch

    The agent swarm future is real. It’s coming faster than most people in the boardroom are admitting and slower than most people on Twitter are claiming. And it’s going to need a lot of foremen.

    Not millions. The leverage is too high for that. But thousands of these roles, well-paid, in every meaningful industry, sitting at the seam between an autonomous fleet of small models and a human business that needs the work done correctly. The companies that figure out how to find these people first and hire them first are going to run absolute laps around the companies that try to do it with a vendor and a procurement process.

    I’m one of these humans. Pinto is one of these humans. There are more of us than the job listings suggest, because the title for what we do hasn’t been written yet. So here’s a working draft: AI Field Operator. Wire and fire guy. Human in the loop. Agent foreman. Pick whichever one lands.

    If you’re already doing this work — even unofficially, even on the side, even just for yourself — you’re early. Build your reputation now. Write up what you do. Show your receipts. The market is about to find you.

    And Pinto: this one’s for you, brother. Thanks for showing me what the next twenty years of this work is going to look like. Wire it up. Fire it off. Same as it ever was.

  • The Digital Tailor: Why the Next Great Tech Job Looks Nothing Like Tech

    The Digital Tailor: Why the Next Great Tech Job Looks Nothing Like Tech

    Tygart Media Strategy
    Volume Ⅰ · Issue 04Quarterly Position
    By Will Tygart
    • Long-form Position
    • Practitioner-grade

    There’s a moment in every fitting room that has nothing to do with fabric.

    The tailor doesn’t ask what color you want. Not yet. First, they ask where you’re going. Who will be in the room. Whether you’ll be standing all night or seated at a table. Whether this is the kind of event where people remember what you wore — or the kind where they remember what you said.

    The clothes come last. The understanding comes first.

    I’ve been building AI systems for businesses for the past two years, and I’ve started to realize that what I actually do has very little to do with technology. The job that’s emerging — the one that doesn’t have a name yet — looks a lot more like a Savile Row fitting than a software deployment.

    (more…)