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.

Part 2: The Thing Nobody Warns You About

Part 2 of 8 · Becoming Agent-Native

An 8-part series on going from delivery team to agent-native organization — lessons earned, not borrowed.
Genesis · → Anxiety · Names Matter · Proof of Value · The Pivot · Co-Creation · The Garage · The Flywheel


Most AI transformation stories skip Phase 2.

They go from “we built some agents” straight to “adoption soared and everyone loved it.”

That’s not what happened for us.

When we introduced the first agents to the delivery team, the reaction wasn’t excitement. It wasn’t curiosity.

It was anxiety. Real anxiety. The kind that doesn’t announce itself clearly. It comes out as skepticism, low usage, polite questions with an edge underneath them.

The edge was: is this going to replace me?

Nobody said it that way. But it was in the room.

There was a second layer too. A small squad had gone off and built things, and now those things were showing up in workflows that had been their workflows. It kind of felt like change being done to them.

“I feel like I’m on the outside looking in at my own job being replaced.”

That’s not a technology problem. That’s a trust problem. Technology solutions don’t fix trust problems.


We made two structural choices. Both matter.

The first choice: we doubled down on agents being teammates, not tools; we gave them all personas and personalities.

This sounds like semantics. It isn’t.

A tool is something you use, or don’t. It sits there. It doesn’t get better. It doesn’t respond to coaching. It doesn’t care if it’s valuable or not.

A teammate is different. A teammate can be given feedback that actually changes how they work. You can advocate for them. You can push for them to be more capable. You have a stake in whether they succeed.

When people have a stake in something, they engage with it differently.

The second choice: every agent got a name.

Reese. Casey. Theo. Mona. George.

Not product names. Not “AI Assistant v2.3.” Real names, each one tied to the function, each one introduced the way you’d introduce a new hire; with context, with expectations, with a clear path to give feedback.

More on this in the next post, but the short version: named agents get coached. Unnamed tools stay static and get ignored. When was the last time your garden rake got an upgrade?


The anxiety didn’t disappear overnight. But it had somewhere to go.

The question shifted from “is this replacing me?” to “how do I best work with this?”

That’s the crack in the door. That’s what Phase 3 is about.

You can build the best agent in the world. If your team doesn’t trust it, you’ve built nothing of value.

Next: Why naming your agents isn’t branding; it’s adoption strategy.

Remember the TCP/IP Stack Wars?

Same Frenzy, New Plumbing

In the early 1990s, “networking” on a PC was a jigsaw puzzle. You didn’t have TCP/IP. You assembled TCP/IP:

  • The right stack
  • The right network card
  • The right driver
  • The right OS version
  • The right configuration (that you’d only discover was wrong at 2am)

If you’re too young to remember this, imagine that the new thing for your work PC was to connect others send messages…but email only worked inside your company, and was not connected to the internet. You carried a briefcase full of papers home if you needed to work on something after hours. You probably didn’t have a mobile phone, and if you did it was mounted in your car – and only made voice calls.

I worked in customer support at a company that lived at the intersection of hardware, software, and networking. Our application ran across multiple protocols, so we didn’t just watch customers struggle—we helped them fight the puzzle: stack + driver + NIC + OS + application. It wasn’t just technical complexity. It was market immaturity.

This is the LLM market today.


Act I: Monetize the Mess

Inside the company I worked for, leadership was on a path to buy a TCP/IP stack to provide a consistent foundation for our applications.

The absurd part: application teams had to make functionality decisions based on disparate network stacks. Test teams had to test them all. Users had to understand them to get them to work. Then someone kicked the cable out of the adapter under their desk and the whole network went down.

Have you tried Hummingbird and Chameleon on both EtherLink and NE2000? What about when the network has both BNC and 10-BaseT connectors?

Networking vendors made tons of money in the confusion…and the switching costs…and the new versions. I believed the stack (and maybe the network cards) were heading toward commodity status. Essential, but not differentiating. I argued against buying or building a stack.


Act II: Standardize It (the Part Everyone Forgets)

TCP/IP didn’t win because one vendor’s stack was the best. Ethernet was technically deficient to Token Ring. But they both won because they became the standard—and standards create gravity. Once the interfaces stabilized, the application didn’t care whose stack you bought.

That’s the key idea: the application shouldn’t care. The user shouldn’t care. Maybe the IT department cares for a while, but eventually just procurement cares.


Act III: Forget It’s There

Once TCP/IP became “default” and the interfaces stabilized, the market stopped paying premiums for stacks.

Networking wasn’t eliminated. Thinking about networking was eliminated. Who knows which network adapter is in their new laptop today? Can you imagine buying a laptop without connectivity? It’s unthinkable.

History continues to prove the pattern:

Monetize → Standardize → Forget


The LLM World Is in Its “Stack Wars” Era

Today’s LLM discourse sounds like the early 90s networking discourse—just with better fonts and worse certainty:

  • Which model for which task?
  • Which provider is “best”?
  • Should we build our own?
  • How do we avoid lock-in?

It’s the same jigsaw puzzle, modernized:

model + prompt style + tooling + memory + safety + cost + latency

And it produces the same executive temptation:

“Let’s build the stack so we control our destiny. Everyone is doing it; we don’t want to be left behind!”

The value today, in the complexity phase, is the model. The value in the future is the thing that uses those models.


Segmentation: Specialized Providers vs Specialized “Application-Layer Engines”

Yes—there are real segments emerging: coding, personal info management, health, and more.

But the more important question is where specialization will live:

Path A: Specialized model providers dominate each segment

“Best model for coding.” “Best model for health.” “Best model for XYZ.”

Path B: Commodity base models + specialized implementations on top

Fine-tunes, adapters, retrieval, tool-use, memory, evals—packaged as product capabilities. In applications.

Path B is the historical match.

The winning move is applications standardize how they connect to intelligence, and the model selection becomes invisible plumbing.

That’s the interoperability point—and it’s where network effects quietly return.


Network Effects: TCP/IP Interoperated with Networks. LLMs Interoperate with Tools.

TCP/IP’s network effect was obvious: the value came from being able to talk to other networks.

LLMs don’t inherently need to “talk” to other LLMs. They compete on capability.

So where’s the network effect?

It moves up one layer.

That’s why protocols like Model Context Protocol (MCP) matter: they standardize how AI systems connect so developers don’t rebuild bespoke, model specific integrations.

Once connectivity is ubiquitous, the LLMs start to disappear.

Phase 1: Value = Plumbing
(TCP/IP stacks | LLM providers)

Phase 2: Value = Interfaces
(Winsock | MCP)

Phase 3: Value = Outcomes
(Connectivity | Apps & Agents)

The Pattern Is Undefeated

Every platform shift starts the same way:

  • We monetize the complexity.
  • Then we standardize the plumbing.
  • Then we forget it was ever hard.

TCP/IP stacks were once a market category. Now they’re invisible.

LLMs are in their stack-wars era.

The winners won’t be the companies with the prettiest model demo…or even the best model. They’ll be the ones who make magical apps and let you forget the model exists.

The Compound Interest of Productivity

AI Agents Are the New Leverage

Productivity tools have long promised to make work easier.
Most deliver accumulation—you stack features, shortcuts, and automations, each adding a marginal gain.

Helpful? Sure.
Transformational? Not really.

But what if, instead of stacking, we could compound?

That’s what AI agents offer

From Tasks to Systems

Start with the small stuff:

  • An agent that filters email.
  • Another that summarizes meetings.
  • A third that drafts follow-ups.

Alone? Nice-to-haves.
Together? They form a system.

The output of one becomes the input of the next. A peloton, not a solo rider. And once that loop forms, you’ve crossed a threshold—from isolated tasks to an adaptive system that gets smarter with each pass. This isn’t just automation—it’s orchestration.

Agents don’t just execute. They learn. They adapt. They cooperate.

Each one adds leverage. Each one amplifies the others.

You’re not saving time. you’re building momentum.

The more agents you connect, the more capable the system becomes.
You go from incremental gains to exponential lift.

Accumulation vs Compounding

We started with a handful of lightweight agents. One evaluated incoming requests and scored them. One updated case notes and status. One checked case hygiene. Another populated task lists and project plans.

Individually? Fine. Connected? Something else entirely.

Work got faster. Work scaled to more customers. Work got smarter.

The system started making decisions:

  • with better quality
  • with greater outcomes
  • across more customers

We’re crossing a threshold in how we work. Productivity isn’t about brute force anymore—
It’s about systems that interoperate and learn.

Agents are the first technology that mirrors how nature builds: organically, iteratively, through networks that adapt and strengthen over time. Each agent you create doesn’t just add function—it adds force.

Future-proof your productivity, start building small agents today.
Connect them.
Let them learn.
Let them compound.

The AI Revolution is here – and it is the savior

I saw some of the fallout of the interview with Dario Amodie yesterday and one of his key attention grabbers was:

“unemployment will spike to 20% in the near future”

On my team, we’re driving hard and fast into onboarding agents (more on that soon), and in doing so, we’re building earned wisdom, not just hypothetical or philosophical views.

His statement made me pause—not because it’s dramatic, but because it’s directionally right and emotionally wrong. There’s a better thought exercise to pursue:

AI won’t just disrupt jobs – it will accelerate the creation of their replacement.

The uncertainty around AI is due to the rate of change—and how fast that rate is itself accelerating.

If you consider past paradigm shifts, they all disrupted the existing workforce massively, but slowly:

  • The Mainframe
  • The PC
  • The Internet
  • Mobile
  • The wheel, fire, electricity…

These changes all transformed industries. They put people out of work—but not forever. No one today is training to be a switchboard operator. People adapted.

The fear with AI, especially Agentic AI, is that those changes are happening in days or weeks instead of years or decades. But this isn’t like past tech waves where new roles emerged slowly.

The technology that is disrupting everything is part of everything.

This means that AI will help design, build, and onboard the future of work in real time. It will empower people to adapt faster, create faster, and solve problems from every angle—not just the top down.

This is the key difference that gives me great hope from working in real time with AI ; the disruptor is the savior all in one, and it brings the power to help those that are disrupted.

AI is driving disruption centrally within organizations, but we are adapting in a decentralized way – AI is enabling those that are getting on board to create systems, training, opportunities, and resiliency for the new future.

This is the first decentralized industrial revolution. Don’t miss it

Dante Alighieri Quote: “Wisdom is earned, not given.”