Checking AI's Work Has Become an Unpaid Part of Your Job

Akamai shipped a product this week called Workforce Protector, after its own threat researchers flagged enterprise AI risk. The Wall Street Journal detailed how North Korean operatives faked their way into U.S. companies. No Jitter catalogued six distinct workplace disputes over who actually owns AI output. And Colorado's AI rules moved from statute to compliance calendars.

Most coverage of AI at work is about subtraction: which tasks get absorbed, whose headcount gets cut. Those four stories are about addition. Somebody has to check the output, sign off on it, prove where it came from, and confirm that whoever produced it is a human employee and not a model, a contractor or a fraud. We'd call that layer accountability. It's landing on individual contributors, almost nobody measures it, and we think it explains a lot of the gap between promised productivity gains and what people actually feel at their desks.

Reviewing the output got harder than writing it

Every honest engineer using a coding assistant describes the same rhythm. Generation dropped to seconds, and review expanded to fill the time saved. The net gain is real but far smaller than vendor slides suggest, because reading unfamiliar code for subtle wrongness is more taxing than writing familiar code correctly.

That's part of why engineering roles keep proving more resilient than predicted. Reporting earlier this summer found engineering among the most durable functions in the current market. Models can write code. Someone with judgment still has to own what ships. The same shift is spreading into marketing, legal ops, finance and support, where the job is moving from producing a first draft to defending a final one.

The Conversation's piece on why AI training can backfire for older workers hits the same blind spot from another angle. Training programs teach prompting and tool mechanics. They rarely teach a verification protocol, when to escalate, or how to document that you checked. So workers with two decades of domain judgment get lessons in typing prompts, then get blamed for slow adoption when the training skipped the part of the job that actually got harder.

Then there's ownership, and the No Jitter piece reads like a preview of the next two years of workplace friction. When a model drafts the code, the contract clause or the customer email, the follow-up questions are about attribution, liability and credit, and most companies have no written answer. Whose name goes on model-assisted work in a promotion case? Who absorbs the cost when a hallucinated figure reaches a customer, a regulator or a board deck? Do the prompts, fine-tuning data and generated assets belong to the employer, the vendor, or nobody? Which clients and auditors have to be told that AI touched the work?

These fights already show up in performance reviews, in IP audits during due diligence, and in the awkward moment when a client asks whether a deliverable was made by a person. Business Insider reported this week that being 'good at AI' may determine your next raise, while nobody agrees on what that phrase means. Everyone is accountable for AI output. Nobody has defined what that accountability involves.

Identity checks and compliance paperwork are landing on you

The North Korea reporting is the reason identity verification is about to become a routine annoyance in every remote engineering org. If fake candidates can pass video screens and ship plausible code, the response is more identity checks, more device attestation, and more hoops between you and your repository access.

Akamai timed the Workforce Protector launch alongside its own AI threat research, which tells you where enterprise budgets are heading. Vendors are selling tools to answer one question at scale: is the person or agent taking this action actually authorized to take it? For you, that means new daily friction and new record-keeping. Expect to log which model touched which artifact, and expect that log to matter during an incident.

It also means sharing your credentials with software that acts for you. If an agent has your permissions, your name is on its mistakes. Most teams never consciously chose that arrangement. They're adopting it anyway, because the productivity story is easier to tell than the governance one.

Regulation pushes the same work onto desks. Colorado's rules, tracked in this week's AI Footprint roundup alongside grid queues and young-worker hiring friction, require impact assessments and disclosure for consequential automated decisions, and that documentation lands on the teams that built and run the system, well beyond the legal department. HR is formalizing faster than most technical teams realize. Improv launched an industry research initiative this week to benchmark AI adoption across HR, HCM and workforce management, which is what happens right before adoption claims start getting audited. Vietnam's target of AI skills for every university student by 2030 suggests the people entering these roles in three years will arrive with tool fluency assumed, so governance literacy is what will set them apart.

Don't overread the layoff backdrop. Tech cuts in 2026 have already passed the full-year 2025 total, and companies from Monday.com to Robinhood have credited AI efficiency with varying credibility. For daily work, the more telling detail is that surviving teams are smaller and each person carries more of the checking and compliance load.

Job ads reportedly pay 62 percent more when they mention AI skills, but the word costs nothing to type. What's scarce is the ability to run AI-assisted work through a process that holds up when a client, an auditor or a regulator looks closely. That's documentable, it's promotable, and almost nobody has it on a resume yet.

So treat it as a specialty instead of a tax. Write down your team's standard for checking AI output, including who checks what and what never ships unreviewed. If nobody has one, drafting it is about as visible as work gets. Keep provenance records for AI-assisted deliverables, because when an ownership or liability dispute arrives, the person with the paper trail wins the conversation. Before adopting an agent, ask what it can do with your credentials, what gets logged, and whose name goes in the audit record. And put review time into your performance narrative, since "caught three production-bound errors in model-generated code" reads a lot better than "used the assistant heavily."

If you're mid-career, skip the generic prompting course. Push for training in evaluation, red-teaming and compliance documentation, where your domain judgment compounds instead of getting flattened.

The people who formalize this work get hard to replace, because they're the reason their company can use AI at all.