The CIO, the CTO: Two Jobs That AI Rewrites

A short history of the two roles, and why the real story isn’t whether they merge. It’s how AI transforms both, pulling one toward the CFO and forcing the other to change its middle letter.

A couple of times a month, somebody asks me a version of the same question. With AI changing everything, should the CIO and the CTO become one job? Or should they split further apart? In an AI Native world, do you still need both roles?

It is the wrong question. It argues about boxes on a chart when the thing actually moving is the work. The more interesting question is how AI transforms each role.

Where the CIO actually came from

Nobody set out to invent the CIO. The role backed into existence.

In the 1950s through the 1970s, a company that owned a computer (yes kids some companies had “a computer”) had a Data Processing Manager. That person ran the mainframe, made sure payroll and inventory came out the other end, and kept the machine in the cold room humming. It was a deeply technical and operational job. It wasn’t strategic, it was closer to running the boiler room than running the business.

As the systems sprawled, the title grew up into Management Information Systems Director. Same core job, wider scope. You were not just keeping the machine alive; you were implementing business applications and occasionally handing management a report they could not have produced themselves.

The term “Chief Information Officer” showed up in 1981 in a book by William Synnott and William Gruber. Their idea was radical for the time: that information itself was a corporate resource, on par with capital and people, and it deserved a seat next to the CFO and the COO. The CIO was the person who managed information as an asset.

Then four decades of forcing functions did the rest. ERP. Y2K. The internet. Email. Security becoming existential. Cloud. Every one of those moved the CIO further from the cold room toward the boardroom. “What great looks like” for a CIO went from “the batch job finished on time” to “the systems are reliable and secure and cost what they should” to eventually, “technology is making the business measurably better.” The job kept getting more strategic, but its core never changed: the CIO is accountable for the machine running. Reliable, secure, governed, affordable. Run the business.

Where the CTO came from, and why it was different

The CTO arrived later and from a different direction.

It grew up in the 1990s, mostly inside technology and product companies, and it started as the chief engineer or chief architect. The person who owned the technology vision and the R&D bet. In a software or tech hardware company, the CTO owned the thing you sold. They lived next to engineering and product, and their job was to be right about where technology was going.

When the CTO title migrated into ordinary enterprises (banks, retailers, manufacturers), it kept that outward, forward lean. In a bank, the CIO keeps the core banking systems stable and secure. The CTO is the one pushing the mobile app, the new platform, the AI bet. One is pointed inward at operations and employees. The other is pointed outward at products and customers.

That is the cleanest way to differentiate the two: the CIO runs the business, the CTO changes the business. Reliability versus reinvention. Operate versus explore. The drawer where the tools are organized, versus the workbench where the new thing gets built.

The reason these were two seats was never the titles; it was because the two motions pull against each other. One optimizes for “nothing breaks.” The other optimizes for “we break things to drive change.” Put both functions in one place with one budget and one set of incentives, and someone always loses. Usually invention loses, because the pager goes off for outages, not for missed opportunities.

What happens next

I’ve thought about this a lot; there are many ways to navigate what’s coming next, but the most effective model is:

The CIO focuses on run – operations at scale is critical, and that has to be their success metric. With almost all infrastructure available through cloud and AI providers, the CIO’s best partner is the CFO.
The CTO changes their “T” – they must understand technology, but their role is to lead the Transformation to AI Native. The CTO has to pivot, their new best partner is the CPO, the Chief People Officer. This isn’t about choosing a technology stack anymore, this is about your people transforming.
Everyone has to embrace the change to AI Native – you either lead the change or it happens to you.

Start with the CIO, because the easy mistake is to read “shrinking footprint” as “shrinking importance.” Cloud did not make the CIO less important, it made the role more manageable. Those are different words. The surface area shrank, but importance held. When the machine is something you rent rather than something you build, the hard questions become consumption and cost: what are we paying, what are we actually using, are the vendors earning what we’re paying them. That is why the CIO’s center of gravity drifts toward the CFO. Run becomes a financial discipline.

The CTO is the opposite shape. It was always the forward-looking seat, and here is the part we never said out loud: a lot of what made that work was that change used to be slow. You could see a trend coming, and you had the time to aim at it. The great CTOs aimed a little faster, and made high value bets. You could be early, be a little wrong, and reposition before it cost you. Vision paid off because the future arrived at a walking pace. The CTO got to be the person who saw it first and had time to act.

That slack is gone. AI is not a new tool on the bench, it is a whole new workshop. It is an accelerant on the rate of change itself, and the rate is compounding. The future is no longer arriving at a walking pace; it is arriving faster every quarter, soon to be every month, then every week. In that world the value of the CTO stops being “pick the right technology” and becomes “build a company that can absorb a new future on repeat.” Vision stops being a telescope you look through occasionally and becomes flight controls you never take your hands off.

The best CTO today needs to see a little further down the road than everyone else and get the company ready for change. That is the whole job. Nobody sees past the event horizon. Anyone selling you a five-year AI roadmap right now is selling you snake oil, because we are wildly early. My first cell phone was a handset bolted to the console of my Chevy S-10 with a cord running into the dash, and I was sure I had arrived. We are at that phone-in-the-truck stage with AI, and the road past the next curve does not exist yet. So the job is not prophecy, it is keeping a fast-moving, low-visibility vehicle pointed in the right direction, and building an organization that can take the next turn without spinning out.

The CTO has to raise their view and widen their aperture. They have to walk fully out of the research lab they were born in. The job is no longer to own the technology, it is to own what the technology does to the company: not the resident expert on the new tech, but the person accountable for the whole organization metabolizing a new future on repeat, faster than it is comfortable doing it.

A different job needs a different partner. This is the tell that the role has changed, not just relabeled. The old CTO was wired into the technical core. In the enterprise that meant standing shoulder to shoulder with the CIO and engineering, vision next to operations and product. The CTO’s closest alliance was with other technologists.

Becoming AI Native is not a technology problem; it is an organizational one. The new alignment is CTO and CPO, vision next to organizational design. One side reads the road and decides where the company has to go. The other side rewires the org so it can get there, reshaping teams, retraining people, changing how the work is divided so the whole company can accelerate with AI. Org charts, span of control, promotions, incentives, budgets – it all has to change. You have to build a company that can absorb change at pace, and you build it through people. A transformation seat that stays glued to the CIO and the engineering org is solving the old problem, stuck in the past. The one that pairs with the CPO is solving the new one.

How both roles transform

So, back to the question: Merge or Split? Neither. That was always the wrong frame, because both options try to bolt AI onto the org you already have, keeping the boxes on the chart and adding “…with AI” to each one. AI is an “empty the cup” moment.

The CIO does not disappear, it tightens. Run becomes a sharper, smaller, more financial discipline that holds the guardrails and watches the meters, shoulder to shoulder with the CFO. The CTO does not disappear, but thrives when they walk out of the lab, partner with the CPO, and transform how the whole company works.

The companies that win the next decade will not be the ones that picked the cleverest org chart or the coolest technology today. They will be the ones that understood, early, that AI Native is not a tool you adopt but a change you lead. You either drive the change, or it happens to you.

Seven Clicks a Day, Forever

I built a three-skill pipeline so I’d never have to “Download transcript” again. The pipeline isn’t the point.

I record a meeting with myself every day.

It’s not a meeting. It’s me, at my desk (or in the car, or on a run), talking out loud to Teams for fifteen or twenty minutes about what I’m trying to do that day: the threads I’m pulling on, the half-finished decisions from yesterday, the things I’m worried about, the projects I need to start or finish, the things I’m excited about. I hit record, I talk, I hang up.

Then Teams transcribes it. And that transcript is gold. Because it isn’t notes I had to type. It’s me, thinking out loud at the speed of speech, in my own voice, with all the messy connective tissue that I never bother to write down. It is the single richest piece of context I can give to my agent for the rest of the day.

But to get it into the agent’s hands, I had to do this every time:

Open Teams.
Find the meeting in the chat.
Click into the recap.
Open the transcript pane.
Click the three dots, click Download, pick .docx.
Convert to txt so the agent can really consume it.
Move the file from Downloads into the folder my agent watches.

Seven clicks. Every day. Forever.

That’s the kind of friction that kills a workflow before it has a chance to compound. I knew it. I felt it every morning. And every day I did those clicks anyway, because the payoff was worth it. Many days, more than once.

I’ve done this for months, and couldn’t build a way around it, until now.

The real point isn’t the meeting

I want to be honest about what this project actually was, because the surface description (“I automated Teams transcript downloads”) sells it short.

The point is not that downloading a Teams transcript is hard. It isn’t. It takes less than a minute.

The point is that a manual task done every single day, forever, is the single most expensive thing you can put in your workflow. Not because of the minute it takes. Because of the decision cost: the tiny moment every time where I have to remember to do it, choose to do it, and then mentally context-switch out of “I’m about to work” into “I’m doing data plumbing.” That decision cost is what kills the workflow. The transcript stops happening. The agent stops getting the context. The agent’s outputs get worse. I notice. I get frustrated.

The fix wasn’t a better agent. The fix was making the input arrive without me.

The constraint that shaped everything

The clean version of this is one Graph API call. GET /me/onlineMeetings/{id}/transcripts/{id}/content returns the .docx. It’s a one-liner.

It’s also blocked in my tenant. Admin consent on OnlineMeetingTranscript.Read.All is not happening. Nobody is going to grant that permission to one rando in a 200,000-person tenant. The answer was no, and the answer is going to stay no, and that’s fine; I’m not building a product, I’m building a workflow for me.

So the entire design had to assume that the only reliable way to get a transcript is to drive the Teams web UI like a human would. Playwright. Click the buttons. Wait for the download. Move the file.

Once you accept that constraint, the shape of the solution starts to come into focus.

Three skills, one pipeline

I broke the work into three pieces, each doing one thing, each independently useful.

Stage 1: /transcript-watcher polls my OneDrive Recordings folder every thirty minutes. Teams puts a new MP4 there every time a recorded meeting ends. When the watcher sees a new file whose name matches one of my watched series prefixes, it knows there’s a transcript to go get. It pings me in Teams (“I see a new recording for X, going to grab the transcript”) and invokes the next skill. The expensive thing (Playwright) only runs when there’s actual work.

Stage 2: /meeting-transcript is the Playwright driver. It opens Teams in a real browser, finds the meeting in the chat, opens the transcript pane, clicks Download, waits for the .docx, and saves it to a watched folder with a sensible filename. This is also the skill I can invoke directly when I want a transcript for a meeting that happened weeks ago.

Stage 3: /doc-watcher watches the folder. When a new .docx lands, it converts it to plain-text Markdown using Word COM (headless), pings me in Teams that it’s ready, and the original .docx never gets touched.

End to end: about thirty minutes typical, an hour worst case (the recording has to finish processing in OneDrive before stage 1 can see it). Zero clicks from me. If I want it right now (I usually do), I kick it off manually. Soup to nuts in three minutes.

The Markdown file lands in the same folder my agent reads at the start of every conversation. The agent sees this as ambient context. It knows what I was worried about. It knows what threads I’m pulling on. It knows what I’m trying to do that day. I didn’t have to tell it. I just had to talk.

The gotcha that confused me

One detail almost broke the whole thing.

The Recap picker in Teams shows the scheduled meeting time, not the actual recording start time. So my standing one-on-one with myself is scheduled at 18:45, but I actually hit record at 12:20, and the file the pipeline produced was named with 18:45. I do this more than once a day, so multiple files collided because they were all “scheduled at 18:45.”

The fix was to stop trusting the Recap picker and read the recording start time out of the chat thread instead. Three regex cases (English UI, localized variants, edge formatting) cover everything I’ve seen. The skill warns loudly if it falls back to the picker’s value, so I’ll notice if a fourth case shows up.

A workflow you can’t trust is a workflow you’ll abandon. Naming has to be right or none of the rest of it matters.

What this actually unlocked

The transcripts arrive. The agent reads them. I haven’t clicked “Download transcript” once.

What I didn’t expect was the second-order effect. Because the friction went to zero, I started recording more things: short voice memos between meetings, a five-minute postmortem after a hard call, a quick “here’s where I left this” before I close the laptop. All of it ends up as Markdown in the same folder. All of it becomes context.

The pipeline removed seven clicks. Removing those clicks made me record three times as often. The agent’s context window got an order of magnitude richer. Jevons’ paradox in action.

That’s the trade I want to keep making: find the daily friction, automate it down to zero, then watch the behavior on the other side of the friction explode.

The three skills are live

All three are published on SkillWorks if you want to lift them. They’re Clawpilot skills: install them with /install, configure your paths, and you’re running. They should work fine with GitHub Copilot, Copilot CoWork, or other systems with minor modifications. Ask your agent to adjust them.

Teams Transcript Pipeline: the full write-up (the why, the design, the dead ends)
/transcript-watcher: polls OneDrive for new recordings
/meeting-transcript: drives Playwright to pull the .docx
/doc-watcher: converts .docx to plain Markdown and pings you in Teams

Each one is independently useful. The doc-watcher in particular has nothing to do with Teams. Point it at any folder of Word documents and you’ve got a feed of plain-text copies that your agent can use.

Your turn

What’s your seven-clicks-a-day workflow? The one you’ve been doing forever because the payoff is worth it, but the decision cost is starting to fray?

Drop it in the comments. Half the time I see somebody else’s, I realize I have the same one and didn’t notice.

More of what I write lives at signalnotsentiment.com. Lessons from doing, not theorizing.

Phone in the Truck

Why I think we’re nowhere near as far along on the AI curve as it feels.

I’m old enough to remember the time before cell phones.

I had a phone on my desk at work. I had a phone at home, the one with the long curly cord, attached to an answering machine that I rewound by pressing a tiny tape down with my thumb. That was the communication stack. That was it. Different numbers, different functions.

Then I got my first cell phone. And it wasn’t really a phone, not the way you’re picturing it. It was a handset bolted to the console of my Chevy S-10, right next to the gear shift. A cord ran from the back of it down into the dash for power. The buttons were on the back of the handset. You could pick it up and put it to your ear like a regular phone, or you could leave it cradled and hit speaker.

I thought I had arrived.

For the first time in my life, the dead hours of a commute became productive hours. Driving to a meeting, driving to a sporting event, driving home late, I could communicate. I told everyone I knew that this thing had changed my productivity forever.

It had. It just wasn’t what I thought it was.

Every stage looked like the peak from the inside

Then Nokia happened. The flip phone. The bar of soap phone. Auto-answer with a headset jack, which felt like science fiction, because now I didn’t even have to reach into my pocket. If it rang, I started talking. Productivity unlocked. Again.

Then keyboards. BlackBerry. Windows Mobile. Email in my hand. I remember thinking, this is it, this is the force multiplier. What more could you possibly need?

Then the iPhone. Apps. Then data, slow and unreliable at first, but a web browser in your pocket, which was a concept that had not existed before. Then real apps. Then Bluetooth, which we all made fun of until every single one of us was wearing it. Then video calls. Then maps that knew where you were. Then a bank in your pocket. Then a camera better than the one in my closet.

At every single one of those stops, I thought we had arrived.

I was wrong every single time.

The phone in my truck and the phone in my pocket today are not the same product. They are not the same category. They are barely the same species. And the version of me sitting in that truck in 1992 could not have described to you what the device in my pocket is today, because the words for it did not yet exist.

Now look at AI

I have a fleet of agents working for me right now.

One of them triages my inbox before I’m awake. One of them rewrites my drafts. One of them sits on my calendar and resolves conflicts before I see them, then sends me a morning report telling me what it moved and why. One of them builds dashboards on demand. One of them runs a weekly report on how many tokens I burned, how many artifacts got produced, and a rough estimate of how many hundreds of thousands of dollars of human effort would have been required to do all of that two years ago.

I am, by any reasonable measure, more productive than I have ever been.

And every time I demo a piece of this to someone, the reaction is the same. This is incredible. This is the future. We must be so close to the peak.

I keep wanting to agree with them. I have lived inside the productivity gain. I know what it feels like.

But I’ve been through this movie before.

We’re in the S-10

We are not at the iPhone moment of AI. We are not at the BlackBerry moment of AI. I don’t think we’re at the Nokia moment of AI.

I think we’re at the phone in the truck.

I think the version of AI my kids are going to use in a decade is going to be as unrecognizable to us, sitting here in 2026, as a modern smartphone would have been to me sitting in my truck in 1992. The words for it don’t exist yet. The form factor for it doesn’t exist yet. The social patterns for it don’t exist yet. We don’t even have the right complaints yet, and complaints are usually a leading indicator that a category is mature.

The exponential nature of this curve fools us. It feels like we’re moving so fast we must be near the end, when in reality the speed is the tell that we’re near the beginning. Things move fastest when they have the most room left to run.

The fact that the pace has compressed does not invalidate the pattern. It just means we’re going to see the next several stages inside a single career instead of across three of them.

So when somebody asks me where we are on the AI curve, I have a real answer now.

I’m in the truck. The cord goes into the dash. The handset is next to the gear shift. And I am absolutely certain I have arrived, in exactly the way I was certain the last four times.

Your turn

Where do you think we are on the AI curve? Phone in the truck? Nokia flip? BlackBerry? Early iPhone? Something later?

Drop your honest answer in the comments. Bonus points if you can name the feature you’re using today that will look as quaint in ten years as a handset bolted to a dashboard looks now.

Lessons from doing, not theorizing.