On my second day at Blank Metal, I sat in on a live Claude Cowork training, a service our team runs for clients every week. Typically, as a Training Producer, I would be the one running it, but because I was less than halfway into my first week on the job, I was just there to observe. I witnessed participants experience the tricky parts of AI adoption: deciding which tool to reach for, recognizing which parts of their work are delegable, figuring out the right language for prompts. With my one day of experience at the company, I couldn’t help but feel that the people best equipped to utilize these tools were the very engineers developing them.

Yet, by the end of the week, I had used Cowork to build a working sketch of a scheduling solution for Shippy, our internal operation-tracking platform, aimed at real problems we’re seeing in how training gets staffed and scheduled. You might be wondering what changed the Day 2 version of me into the Day 5 one. It wasn’t that I had suddenly acquired a new technical skillset over the course of a few days. What changed was what I’d been given: the tools to work with, the context to use them well, and enough trust to act on what I found. That combination—tools, context, trust—is what let me go from spectator to builder in a matter of days, and it’s a key part of our company culture at Blank Metal.

A company always building itself

Blank Metal’s Training and Enablement team gives clients the tools they need to implement AI effectively, starting with the people we teach to become AI-fluent builders in our trainings. We just celebrated our first birthday as a company earlier this year. Most companies would treat being young as something to outgrow. But we see it as one of our greatest strengths: staying in motion is what lets us keep handing new people something real to build from day one. A system that stops evolving is already behind what the tools underneath it just became capable of. So we don’t get to build something once and defend it. We rebuild constantly, with the same energy we ask clients to bring to their own systems, because staying at the frontier requires it.

Blank Metal’s perpetual quest to do things better and more efficiently with bleeding edge tech is not what surprised me. It was that I was expected to help contribute to it only days into the job. Nobody asked me to wait until I’d “earned” it. Tools, context, and trust arrive together here, on day one, not as something you accumulate tenure toward.

What enablement feels like

Being on the receiving end of the service I provide for others, I noticed precisely what makes it work. Blank Metal doesn’t just hand users a tool and walk away. We make sure people have the context to use it well. My manager, Teresa, gave me a complete picture of the current state of training, including struggles, successes, and what’s being left on the table. That’s a lot more comprehensive than just a task list. In a conversation with our Chief Growth Officer, Elli Rader, she gave me a piece of advice that has completely reframed the way I think about my work: she told me to look at our existing systems with fresh eyes, because the fact that we’ve been doing something one way doesn’t make it the right way. She wasn’t onboarding me—onboarding just tells you where things are. Instead, Elli was actively handing me the judgment to question what I found, not just a map of where to find it. Between Teresa and Elli, I’d already been given two of the three things that make enablement work: context and trust. The tools turned out to be the easy part.

The same tools, different responsibilities

A few days later, I went and took a stab at a working sketch of a scheduling solution for a real issue we had with Shippy using Cowork, Notion and Granola, the same way an engineer might prototype one. Then I checked it against my team’s own written spec for the project, line by line, and found gaps in my first pass. The tools didn’t just help me to produce something fast. They helped me to understand the problem well enough to see exactly where my own first answer fell short, and fix it before anyone else had to point it out. I’m thinking about problems the way our engineers do, because we’re using the same tools as teammates of a shared mission. My responsibilities are just pointed at a different problem: making sure clients and their people can do the same thing with these tools that I just did with mine. And that expectation isn’t a special allowance for a new hire with an engineering background. It’s the same one placed on everyone at Blank Metal, whatever their title or however long they’ve been here: don’t just use the system, improve it.

What this says beyond my own week

I don’t think my week was special because of me. I think it’s evidence of something Blank Metal built on purpose: an environment where the right tools, the right context, and the right trust let a capable person contribute for real, fast, regardless of what’s in their title.

That’s the best argument for AI-native operations, as opposed to “we move fast.” Moving fast is the result. What produces it is a company that treats enablement as infrastructure. I didn’t get here by being unusually capable. I got here by being unusually enabled, with the same tools as everyone else in the building and enough trust to use them from the start. That trust isn’t a courtesy extended to new hires. It’s the same thing that lets Blank Metal operate at the frontier at all: a company that can hand someone real problems on day one is the same company that can move as fast as the tools underneath it change. I happened to be this week’s proof. Someone else will be next week’s.

Last fall, I wrote about OpenAI’s How People Use ChatGPT report. TLDR: 700 million weekly users, message volume/use up five-fold in a year, most of the real action was in everyday writing and decision-making. My argument then was that if you were still only running AI pilots or using AI for just chatting, you were behind.

OpenAI’s economics team hasn’t been quiet since. In April they published their AI Jobs Transition Framework, which predicted that 24% of U.S. jobs are likely to “reorganize” as AI shifts their day-to-day tasks. This was a prediction, though, not a measurement. A few days ago they released the measurement: Work at the Frontier: How AI is expanding what people do at work. Last year’s report was about how people work with AI, whereas this one is about who does the work.

The report draws from 800,000+ work-related ChatGPT messages from U.S. business users across eight functions, with each message mapped to the occupation that task has historically belonged to. This is its key takeaway: 43.5% of occupation-specific messages involve tasks traditionally associated with a different occupation. That includes salespeople running financial calculations, customer experience folks troubleshooting software, and designers doing a bit of everything. OpenAI calls this “task crossover.”

In April I wrote about the Great Reinvention, and the core observation was that the real work isn’t AI adoption anymore, it’s about reinventing how people and companies operate. This is the first large-scale data I’ve seen that catches that reinvention happening in the wild.

Here are five takeaways from OpenAI’s report on how AI usage is reshaping corporate culture:

1. Last fall the story was about adoption. This time is reorganization.

What: Nobody needs the 700-million-users-week metric anymore. The new data (more importantly) shows us what all that usage is doing: dissolving the lines between roles. Excluding generic work like email and scheduling, nearly half of what people bring to AI sits outside their own lane. In five of eight functions it’s a majority: customer experience (77%), design (75%), HR (69%), legal (56%), marketing (53%).

So what: The adoption race is nearly over, employees are using AI. The new race however, the reorganization race, has started and most companies don’t yet know they’re in it. According to OpenAI’s chief economist: “The boundaries between jobs are likely already becoming more flexible due to AI.” Our current org charts are growing outdated.

Now what: Stop measuring AI success by seats and logins. Look at what work actually flows through it, and whether your team structure still matches reality.

2. Job descriptions are snapshots, not boundaries.

What: Roles are becoming task bundles that workers remix daily. The report makes a point I haven’t seen anywhere else: even government occupational data will drift further and further from how work actually gets organized, because it’s built on job descriptions that describe the old world.

So what: Your HR system, your comp bands, and your hiring specs all assume the old boundaries. The document in your ATS describes a job that doesn’t encompass what the person in the role is or will be doing. In April, I wrote that most enterprises are running AI upskilling against a job architecture designed for the information-mover era. This report is what that mismatch looks like in data.

Now what: Audit what your people actually do with AI. The data exists; if you haven’t looked. Rewrite roles around outcomes and judgment, not task lists. Hire for people with a lot of range.

3. Small teams get the biggest advantage from crossover.

What: Among typical users, ~19% of work messages at 2-5 seat workspaces cross occupational lines versus ~16% at 101+ seats. At a small company, there’s no analyst to hand the spreadsheet to. AI is the specialist you don’t have.

So what: Last year I said smaller (potentially cheaper) teams could suddenly compete with your core value prop. The data backs that up. This is the Blank Metal bet: small senior teams that cover a lot of ground because AI extends everyone’s reach. I’m even more certain of this now than ever. Small teams can do huge work!

Now what: If you’re small, lean into this deliberately instead of accidentally. If you’re big, ask why your people aren’t crossing boundaries. In my experience the answer is process and permission, not capability.

4. Marketing and engineering are everyone’s second job.

What: Two kinds of work travel everywhere: marketing tasks (promo materials, campaigns, positioning) and engineering tasks (troubleshooting, scripts, technical explanation). Marketing work alone is ~9% of what non-marketers bring to AI, the highest of any function. And calculating financial data is a top-three borrowed task in every single non-finance occupation.

So what: The accessible layer of every specialty is being absorbed by everyone else. Read the fine print, though: design tasks barely travel at all (1.7%), and engineering’s hard core stays in-house. Outsiders take the approachable layer. What stays inside the specialty is judgment, standards, and the hard 20%. As I wrote in March, taste is the human skill that gets more valuable as AI gets better.

Now what: Give non-specialists rails to do specialist-adjacent work: templates, checklists, escalation paths. Point your specialists at the work that actually requires them: review, standards, and the problems AI can’t carry.

5. The guardrails problem got harder.

What: Last year I argued AI generation needs guardrails because output quality is so uneven (sometimes it’s still garbage). That’s an easy version of the problem. Look at which tasks are crossing: sales teams are now making financial calculations, and customer experience teams are now communicating with government agencies, which is a legal task. That’s not at the same caliber as “help me write an email.” It’s work with lasting consequences, done by people who can’t fully evaluate if the output is good (and legal), or not.

So what: AI makes everything look finished. A CFO reads an AI-built financial model and starts poking at the assumptions, and a salesperson reads the same model and sees an answer. It’s the same document, but two very different reviews, and only one of them can be right. Imagine that a deal gets priced off that model and the foundational assumption/calculation is wrong. Who owns that? Under the old division of labor, the specialist did. With crossover, nobody does, and most companies haven’t noticed that gap. Engineering solved this problem decades ago and called it code review. There is no code review for the pricing model your sales team built last week.

Now what: Take these three steps: Tier the risk: an internal draft and a customer-facing number are not the same review problem. Name the owner: someone qualified signs off on cross-boundary work with real consequences, and that review time counts as real work, not a favor. Teach interrogation, not tools: what assumptions did it make, what would have to be true for this to be wrong, who would know? The companies that build this muscle first get crossover’s speed without its blowups.

What we’re seeing from the front row

We don’t just read this research. Our delivery model embeds small forward-deployed teams alongside client teams, so we watch how work actually flows at dozens of companies. Here are two things we keep seeing:

First, crossover is already normal on the ground. On our engagements, product people ship working prototypes, engineers write the positioning doc, and whoever has the right context often owns (and helps direct) the data modeling. Few people (for better or worse) ask permission to cross a functional line. Rather, those that are succeeding simply ask whether the output held up in review. Those that are REALLY succeeding know when they’ve absorbed the “easy” part of another function and when they’re moving into the parts they shouldn’t be doing (they’re leaving that work to the domain experts).

Second, the roles that are emerging often don’t map to current functions at all. A few weeks ago I pinned a quote from Borris Cherny (creator and head of Claude Code) in our Slack about engineering, product, design, and data science melting into a new kind of role, with archetypes like the Prototyper (churns out ideas, most don’t ship), the Builder (turns a prototype into production-grade product), and the Sweeper (cleans up the UI, simplifies the code). Look at what those archetypes are organized around. They aren’t specialties, they’re modes of working with AI. The question that matters when you meet someone new is shifting from “what function are you in?” to “what are you passionate about and good at - and can you be successful with AI doing that kind of work?”

A caveat

This data is descriptive. It counts messages, not outcomes. It can’t tell you whether the work was good, whether it saved time, or whether AI created these crossover tasks versus surfacing work people were already stuck doing alone. OpenAI is upfront about all of this, and you should be too before you reorganize anything off one report. But the direction is hard to argue with, and it matches what we see in the field every week.

The So What

Last report I closed with “bold today is boring tomorrow.” Months later, here’s what the data says actually happened: while most companies were still debating AI strategy, their people were redrawing the org chart, one message at a time.

That’s the biggest finding in this report. The reorganization isn’t coming. It’s underway, inside companies today. Your people already decided the old boundaries don’t apply to them. The only open question is whether you manage it (review, accountability, roles rebuilt around how work actually flows) or keep pretending the old job descriptions are true while the work moves without you.

Everyone has the same tools now. The companies that succeed during the next twelve months will be the ones that rebuilt the organization around what their people can (and want to) do.

Welcome, again, to the Great Reinvention.