In January, at the World Economic Forum in Davos, Anthropic CEO Dario Amodei told The Economist’s Zanny Minton Beddoes that models could be six to twelve months away from doing “most, maybe all” of what software engineers do end to end. The session was titled “The Day After AGI.” He backed it up with an anecdote. Some engineers at Anthropic, he said, don’t write code anymore. The model writes it and they edit.
Ten months later, a fleet maintenance software company in Glendale, Arizona is trying to hire one of those editors. RTA was founded in 1979 by Ron Turley and his wife, Dixie. Ron started at UPS as a driver and went on to oversee maintenance for more than 48,000 vehicles. The company’s opening for a Senior Software Engineer on its Fleet360 platform is fully remote and pays $200,000 to $220,000 in total compensation. The posting runs about 2,300 words and mentions AI 25 times. Its list of hard requirements starts with five to seven years of owning production software. AI comes fifth.
I read job postings the way I read 10-Ks. They’re one of the few documents where a company commits in writing to what it will pay for, and most are boilerplate because nobody expects them to be read closely. This one was written in the first person by RTA’s head of engineering, and it’s the most precise description I’ve found of what an employer needs once the code writes itself.
The headlines turned Davos into a countdown, with software engineers replaced inside a year. That reading is lazy, and Amodei’s own anecdote contradicts it. His engineers stopped typing. They didn’t stop working there. Editing a machine that writes faster than you can read is a job, and RTA wrote down what that job requires in more detail than anything said on that stage.
One posting isn’t a market. The payroll data agrees with it anyway.
One job description proves nothing about a labor market, and I’m the last person who’d claim otherwise. In May I argued that job-board counts prove nothing either, in a piece titled There Is No Software Engineering Recovery. There Is Only DataAnnotation. A posting is free to publish, and a lot of what gets tagged “software engineer” is AI-trainer gig work. So here’s payroll.
The Bureau of Labor Statistics series for computer systems design and related services, the payroll category that covers custom software and IT services firms, peaked at 2,483,500 jobs in March 2023. The September jobs report put it at 2,355,400. That’s roughly 128,000 jobs gone, and the number has fallen every month since May.
On October 2, CompTIA put out a release headlined that employer demand for tech talent had reached a three-year high, with new postings up 16 percent year over year. Further down, the same release estimated the tech sector lost 10,350 jobs in September. Postings went up. Jobs went down.
The losses aren’t spread evenly. Erik Brynjolfsson, Bharat Chandar and Ruyu Chen of Stanford’s Digital Economy Lab worked through ADP payroll records covering millions of workers and found that by September 2025, employment of software developers aged 22 to 25 had fallen nearly 20 percent from its late-2022 peak. Experienced workers in the same occupations held steady or grew. In February they tested the obvious objection, interest rates, and found it doesn’t explain the gap.
Executives, meanwhile, have started saying it out loud. Challenger, Gray & Christmas counted 120,136 announced job cuts through September in which the employer cited AI. For all of 2025 the figure was 54,836.
So the market is shrinking, the shrinkage is landing on the youngest engineers, and the people announcing the cuts are crediting the machine. That’s what RTA is hiring into.
Only one of the five requirements names a technology
RTA lists exactly five hard requirements, and they fit in one sentence. The company wants five to seven years of real production ownership, genuine full-stack range, Node.js and TypeScript at production scale, deep relational database skill, and AI-assisted development as a daily habit, with judgment. Four of those would have made sense in a 2019 posting.
Look at how little of that is a stack. Node.js and TypeScript are the only named technologies, and even that line is about APIs and services that hold up under real load. The database is never named. Not once. RTA’s own front end runs on Vue, and Vue is listed as a nice-to-have, with React or Angular said to translate well. Queues, caches and containers are all nice-to-haves too, and the specific products are offered as examples. The closing section says it outright. Technology is a means to solve a problem, and the engineer they want doesn’t think of themselves as front end or back end.
That’s the most important signal in the posting. It follows directly from Amodei’s anecdote, because once an agent writes the syntax, framework fluency becomes the cheapest line on a résumé, and the agent already knows Vue’s API better than most of the people who list it. What it doesn’t know is your schema, your incident history, or the parts clerk who’ll be on the phone when the inventory count is wrong. RTA doesn’t care which database you know. It cares a great deal whether you understand one. I publish as the .NET AI guy that ships, and I’ve always assumed the word doing the work was the last one. RTA seems to agree.
The first requirement carries a line worth quoting. RTA wants “not years logged, but systems you were responsible for when they broke.” A model can generate an API endpoint in seconds. It has never been the name on a postmortem. I have no patience for people who make decisions whose consequences they’ll never carry, and a model is the purest version of that ever built. RTA’s 90-day plan expects the new hire to take point on a production incident and drive it through resolution and postmortem. The company isn’t paying for the code. It’s paying for the person who answers for it.
I’m watching what happens without that person at a company I consult for. Departments that got tired of waiting on the dev team’s release cycle started vibe coding their own apps, scraping the company’s own site, pulling straight from HubSpot’s API and querying production databases with read-only access. All of it works. That’s the problem. Every one of those apps is a security review nobody did and a maintenance bill nobody budgeted, and when one breaks, nobody’s name is on it.
Full stack has changed meaning, too. When an agent can write the Vue component, the endpoint and the migration in one pass, the line between front end and back end stops being a way to organize a team. Somebody has to understand all three layers well enough to see which one the agent got wrong.
The database requirement is the one I’d underline twice. RTA wants engineers who design schemas, optimize SQL, diagnose performance problems and reason about integrity and concurrency, which happens to be where generated SQL fails quietly. A query returns the right rows on a development database with a few hundred records and crawls on a production table with millions of them. Two parts clerks reserve the last brake caliper in the same second, and inventory goes negative. I build on SQL Server 2025, which moved vector search into the database engine, so embeddings now live under the same transactions, permissions and backups as the rows they describe. AI pulled more of the work into the database. It didn’t make the database simpler. SQL is still the second most used language in Stack Overflow’s 2026 survey, at 58.4 percent.
The fifth requirement is the only new one, and it reads like a job description for a senior editor. You break the problem down, hand the agent useful constraints, judge what comes back and keep steering until it’s production quality. The clause that matters is the last one. You have to be able to tell when it’s confidently wrong.
The AI section is a manual for catching the machine
RTA calls its “Working with AI here” section the part it cares about most. Nearly every line in it is about verification.
It opens with engineers who work agentically instead of with autocomplete, people who have driven multi-file refactors, migrations and test coverage through coding agents. Then it asks for something most companies haven’t thought about yet. The engineer maintains the context files, rules and prompts that make those tools work for the whole team. That file has a standard now. AGENTS.md, a plain Markdown file that tells coding agents how a repository works, went to the Linux Foundation’s new Agentic AI Foundation in December 2025 alongside Anthropic’s Model Context Protocol. A rules file is infrastructure. It shapes what every agent on the team produces, so it gets reviewed like the code.
RTA also wants AI-generated work treated like code from any other contributor, which means somebody understands it, challenges it and tests it before it ships. Developers have mostly arrived there on their own. Stack Overflow’s 2026 survey of 30,903 developers found 48 percent trust AI output when they can easily verify it. Only 6.6 percent trust it with important work decisions.
That’s why a posting this technical spends three sentences on conflict. RTA wants people comfortable on a team that thrives on healthy conflict, says people with thin skin need not apply, and then adds “No, seriously.” It even offers bonus points per Patrick Lencioni book read. Lencioni’s The Five Dysfunctions of a Team lists fear of conflict as one of the five, and it has quietly become an engineering problem. When an agent can produce a forty-file refactor before lunch, the bottleneck is review, and review is disagreement. Somebody has to tell a colleague, and the colleague’s agent, that forty files of plausible code are wrong. In Stack Overflow’s 2025 survey, 66 percent of developers said AI solutions that are almost right, but not quite, were a problem they ran into. Almost right is the expensive kind of wrong. It survives the first read.
On evals, RTA wants engineers who can explain how they knew an AI feature actually got better “rather than just felt different.” In early 2025, the research group METR ran a randomized trial with 16 experienced open-source developers working 246 real tasks in repositories they knew well. With AI allowed, tasks took 19 percent longer. Afterward, the developers estimated AI had made them 20 percent faster. METR’s February update says developers are probably faster now, then concedes its data is only very weak evidence of how much, partly because so many paid participants refused to work without AI that the experiment stopped measuring what it was built to measure. The tools probably improved. Nobody’s gut got better at telling.
The best sentence in the posting comes after that. RTA’s customers track DOT compliance, warranty claims and parts inventory, and the posting says some of that “must never be answered by something probabilistic.” RTA built its own product to that rule. Ron360, the conversational assistant it made generally available inside Fleet360 on June 11, answers questions from a customer’s live fleet data, cites its sources, stays inside the permissions already configured, and is read-only. It can tell you what the records say. It can’t change them. It also shares a first name with the founder. RTA’s comparison page against AssetWorks carries the heading “AI That’s Shipped, Not Promised,” which is uncomfortably close to my own tagline. The company’s public-sector customers include Utah, Wyoming, Pennsylvania, Massachusetts and the City of Las Vegas, where I live.
This is the part of the job I do every day. I build MCP servers and agents on the Microsoft Agent Framework, including Cara, the shopping assistant on ShopSnap storefronts, and a planning agent for a Las Vegas wedding chapel. In both, the model handles the language and the facts come from tools that query the database. LLMs are slow, so I keep them out of any decision that has to be made fast, and for quick, continuous data reads I skip MCP entirely and call a plain API, which still wins there. Knowing where the model doesn’t belong is most of the architecture.
The last item opens with two words. “Bring proof.” A repo, an eval suite, a rules file, an agent workflow built for a team. The posting says anything real tells them more than another year of experience on paper, and the hiring process backs that up. It’s two conversations, one with the hiring manager about your work and how you use AI tools and one with the team, with no take-home or timed puzzle listed. A degree is marked not required. The whiteboard interview tested whether you could produce code under pressure, and the machine does that now. What’s left to interview for is judgment, and the only evidence of judgment is a record. The posting also discloses that RTA may use AI to summarize and analyze applications before people make the call, so your first reader may be a model. Put the proof where it can find it.
The 90-day plan is a Staff job with a Senior title
Add up the success criteria. By day 90, the new hire has shipped two features end to end, browser through database, and taken point on a production incident through its postmortem. They’ve made one improvement to the team’s AI workflow that another engineer adopted and written down an opinion on part of the Fleet360 architecture they’d change. By the end of year one, they own a significant domain of the product, including architecture, reliability and roadmap input. They’ve shipped an AI-powered customer feature from design through evaluation, measurably improved something the team tracks, and changed how at least two other engineers work, to the point that those engineers would name them as the reason.
That’s a Staff engineer. The title says Senior, and the band tops out at $220,000 in total compensation. On the same job board, a Staff Full Stack Engineer listing from Assured offered $210,000 to $270,000. This isn’t a knock on RTA. A $20,000 band is honest. A Software Architect listing two slots down offered $110,000 to $220,000. The spread is a salary.
RTA is only saying out loud what most employers are already doing. The posting asks for a force multiplier, and from the payroll side that phrase has a precise meaning. When tools multiply what one engineer can carry, employers don’t multiply the pay. They multiply the scope. Every year-one target is something a dashboard can confirm, whether that’s p95 latency on a key endpoint, deploy frequency, flaky test count or defect escape rate. If you can’t describe your last two years of work in numbers like those, you’re interviewing for a job that’s being retired.
Nobody is hiring the people who would grow into this job
There’s no junior version of this posting. The floor is five to seven years, and the Stanford numbers say the market is producing far fewer people who’ll clear that floor in 2031. Every company wants the engineer with production scars. Fewer of them will pay for the incidents that leave the scars.
It’s the same short-term thinking behind the vibe-coding departments, and it ends the same way. CEOs are cutting the bottom of the ladder because an agent closes a junior’s tickets for less, which is true this quarter. In five years they’ll need senior engineers, find out nobody trained any, and pay consultants like me to read code nobody owns. I’ll take the work.
If you’re early in your career, the posting still shows you where the door is. The experience requirement has a minimum. The proof requirement doesn’t. A public repo with a real AGENTS.md, an eval suite for a small AI feature with a number you can defend, a production system you run for an actual user, the postmortem you wrote the first time it broke. Any one of those is worth more than another certification, and I don’t see another way around the floor.
How to survive the day after
I’ve lost more than one job to outsourcing. Every wave of it was sold on the premise AI is sold on now. The code is the cost, so make the code cheaper. What stayed behind each time was the work somebody had to answer for, like the system that fell over at two in the morning, the vendor integration that broke because someone changed an API without telling anyone, or the customer who needed a person in the room who understood their business. The code moved. Accountability didn’t, because you can’t outsource the person who gets fired when it fails.
AI is the same trade at a bigger scale, and the people who come out the other side will be holding something that can break, with their name on it. Take the pager. Write the postmortem. Plenty of engineers have built careers around avoiding exactly the moments that now make someone employable.
Stop leading with your stack. RTA made its own front-end framework optional, and the agent already knows all of them. Lead with what you owned, what broke and what you changed afterward.
Then learn your database properly, meaning execution plans, indexes, isolation levels and what actually happens under concurrent writes. The agent will write the SQL. You’re the one who reads the plan before it ships.
Your AI workflow should be something another person can inspect. Write the rules file your team uses and keep a version in a public repo. Build an eval for one AI feature, even a small one, so you can say it got better by a number instead of a feeling. Automate one piece of real engineering toil, whether that’s test generation, a migration or documentation, and keep the before and after. The posting lists observability as a nice-to-have, and I’d move it into the requirements. When an agent wrote half the code path, logs and traces are the only reliable witness to what it does in production. That’s the job of an observability framework like PLogger, my open-source one.
Domain knowledge decides where the model stops. Money, compliance, inventory and anything an auditor will ask about belong to deterministic code, and the model gets read access at most. RTA knows which of its customers’ questions are too important for probability. The company was founded by a man who oversaw maintenance on more than 48,000 UPS vehicles. You won’t get that knowledge from the model, because the model has no idea which of its answers matter.
The last one is temperament. You’ll be overruled in review, and you’ll overrule an agent that sounded completely sure of itself, sometimes in the same afternoon. Teams that can’t argue can’t review, and a team that can’t review has no business letting an agent near production.
Amodei’s twelve months run out in January.
About the Author
Gal Ratner has spent nearly thirty years building and shipping production software on the Microsoft and .NET stack, from Classic ASP to agentic AI, for clients including Microsoft, Sony, Rockstar Games, 2K Games, Best Buy, and Allegiant Air. He is Head of Artificial Intelligence at Web Business Development and Chief Architect at Prana Entertainment, a Las Vegas enterprise software and AI consultancy. He is also the founder and CTO of Inverted Software and WhiteStar Labs. He was the sixth employee at Break.com and a finalist for the Los Angeles Business Journal’s CTO of the Year.
His current work is agentic AI in production, built on MCP servers, the Microsoft Agent Framework and SQL Server 2025 vector search, including Cara, the shopping assistant for ShopSnap storefronts, and the open-source observability tooling behind PLogger. He writes about AI the way a practitioner sees it, bullish on what the tools can actually do and allergic to the marketing wrapped around them.
blog.galratner.com · linkedin.com/in/galratner · galratner.com
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