The CTO Playbook

AI adoption is getting easier for everyone. The harder question is whether you're using it to optimise the game you're already in, or to build a position that changes the game.

Ritavan, author of The System Gambit and Data Impact, returns to The CTO Playbook after his first appearance on episode 30.

A System Gambit is a deliberate sacrifice that builds a structural advantage that compounds. Rather than optimising within the system you're already in, it changes the system so competitors can't simply buy or copy their way to your position.

Ritavan explains the difference between a System Gambit and a System Anti-Gambit, using Nokia and Amazon as examples. He also explains why adding AI to the data your business already generates tends to keep you inside your existing paradigm.

Adam then puts his own move from fractional CTO work to coaching through Ritavan's three tests: self-improving loops, path dependence, and management logic antagonism.

In this episode, he explains:

■ 𝗪𝗵𝗮𝘁 𝗮 𝗦𝘆𝘀𝘁𝗲𝗺 𝗚𝗮𝗺𝗯𝗶𝘁 𝗜𝘀: Why a deliberate sacrifice now can unlock a structural advantage that compounds.

■ 𝗧𝗵𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗔𝗻𝘁𝗶-𝗚𝗮𝗺𝗯𝗶𝘁: Why optimising inside your current system is rational, and why it leaves you exposed when a rival changes the game.

■ 𝗔𝗴𝗶𝗹𝗶𝘁𝘆 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗧𝗵𝗲𝘀𝗶𝘀: What Nokia shows about reaching peak agility and still losing badly in a market.

■ 𝗔𝗜 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗘𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗣𝗮𝗿𝗮𝗱𝗶𝗴𝗺: Why adding AI to your own business data may bring incremental gains without moving you into a new system.

■ 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲 𝗧𝗲𝘀𝘁𝘀: Self-improving loops, path dependence and management logic antagonism, applied to Adam's own CTO coaching journey.

■ 𝗪𝗵𝗲𝗻 𝗡𝗼𝘁 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗮 𝗚𝗮𝗺𝗯𝗶𝘁: Why a short runway, or a lack of real control over the system, makes it the wrong move.

Build your own CTO Playbook at www.theCTOplaybook.com, the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

You'll Learn:

[0:00] Introduction

[3:07] Why Strategic Advantage Requires a Sacrifice

[5:34] Why a System Gambit Isn't a Gamble

[8:37] The System Gambit vs the System Anti-Gambit

[9:38] What Nokia Shows About Incremental Optimisation

[13:41] What Actually Qualifies as a Paradigm Shift

[23:12] Why AI Alone May Reinforce the Existing Paradigm

[26:25] How Amazon Built Structural Advantage

[29:37] Testing Adam's Own System Gambit

[33:05] Applying the Tests Live

[38:18] How Cross-Coupled Loops Create Compounding Advantage

[40:29] Why Path Dependence Makes a Strategy Hard to Copy

[46:42] When the System Anti-Gambit Is the Right Move

[50:45] When You Actually Need a System Gambit

[53:03] The Three Tests: Self-Improving Loops, Path Dependence and Management Logic Antagonism

[55:39] Where to Find Ritavan

Follow Ritavan:

■ Book: https://www.amazon.com/dp/B0GY8J23SK

■ Substack: https://systemgambit.substack.com

■ Website: https://ritavan.com

■ LinkedIn: https://www.linkedin.com/in/rritavan/?isSelfProfile=false

■ YouTube: http://www.youtube.com/@UCKf6F9wfQH56mCphHdbnCYg 

The CTO Playbook:

■ Build your own CTO Playbook at https://www.thectoplaybook.com, the leadership platform built for the full CTO journey, coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

■ Follow Adam: https://www.linkedin.com/in/adamhorner/

■ YouTube: https://www.youtube.com/@TheCTOplaybook

■ Prefer listening? Catch the podcast: https://bit.ly/thectoplaybook

AI isn't just changing how engineers work. It's forcing technology leaders to rethink what makes management valuable.

Prof. Charles Wood is Senior Manager, Data Science & AI at Comcast, where he has spent nearly a decade leading enterprise AI strategy, and the author of Artificial Intelligence Management. Most of the AI and jobs conversation focuses on developers.

Charles looks at the layer above them. His argument is that the qualities that make a manager good, like consistency, fairness and a repeatable process, are exactly what a language model can reproduce. He explains why the more predictable management work becomes, the easier it is to automate. He also covers why implementing AI means nothing until you prove the impact, and why the managers who stay valuable will be the ones creating outcomes that can't be reduced to an average.

In this episode, he explains:

■ Why Middle Management Is Exposed: Why predictable, repeatable, standardized management work is easier to automate than engineering itself.

■ AI Is Arriving Top-Down: Why adoption is coming from the executive level, and why it has to start with business value.

■ Precision, Personalization and Scale: What AI makes possible, and where hallucinations, testing and human oversight still matter.

■ Stop Asking "Did We Implement AI?": Why every AI initiative needs measurable outcomes, and why the real question is "So what?"

■ Are You Average?: How managers can use AI to expand their scope, take on more complexity, and deliver results no model can replicate.

■ The New AI Playbook: Charles' principles for rebuilding your leadership playbook, starting with continuous learning and the willingness to nuke your old one.

Build your own CTO Playbook at www.theCTOplaybook.com, the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

You'll Learn:

[0:00] Introduction

[5:10] Why AI Could Disrupt Middle Management More Than Engineering

[9:42] Why Predictable Management Work Is Easier to Automate

[14:18] What LLMs Can and Cannot Replace in Technical Teams

[19:06] Why AI Adoption Has to Start With Business Value

[24:15] Hyper Precision, Personalization and Scale

[29:37] Hallucinations, Testing and Human Oversight

[34:21] Why Every AI Initiative Needs Measurable Outcomes

[39:04] Using AI to Expand Your Scope and Stay Relevant

[44:12] Continuous Learning as a Leadership Requirement

[49:06] The Five Principles of the New AI Playbook

[54:18] From "Did We Implement AI?" to "So What?"

Follow Prof. Charles Wood:

■ Book: Artificial Intelligence Management

■ Convergent Lens: https://convergentlens.com

■ LinkedIn: https://www.linkedin.com/in/profwood/

The CTO Playbook:

■ Build your own CTO Playbook at https://www.thectoplaybook.com, the leadership platform built for the full CTO journey, coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

■ Follow Adam: https://www.linkedin.com/in/adamhorner/

■ YouTube: https://www.youtube.com/@TheCTOplaybook

■ Prefer listening? Catch the podcast: https://bit.ly/thectoplaybook

Quantum computing may not be breaking your security today, but the decisions you make now could determine how exposed your organization is when it does. Conor Deegan explains why CTOs need to start preparing before Q Day arrives.

Build your own CTO Playbook at www.theCTOplaybook.com, the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

My guest this week is Conor Deegan, a security engineer, cryptographer, and co-founder of Project 11, focused on helping organizations prepare for the transition from classical cryptography to post-quantum cryptography.

The quantum threat goes far beyond Bitcoin and digital assets. Conor explains how quantum computing could eventually affect the cryptography behind banking, communications, cloud infrastructure, authentication, health records, and everyday internet security.

He breaks down what "Q Day" means, why organizations cannot simply wait until a powerful quantum computer exists, and how "harvest now, decrypt later" could put sensitive information collected today at risk in the future.

The transition also comes with real engineering challenges. Post-quantum cryptography can require more computing resources, memory, storage, and bandwidth, while legacy systems and constrained devices such as IoT hardware can make migration even more difficult.

Conor then shares a practical framework for CTOs: understand the threat, put someone in charge, identify critical systems and data, take the easy wins, put pressure on vendors, and stop creating migration debt.

You'll Learn:

[0:25] The Quantum Threat and Why It Matters

[3:54] Understanding Q Day

[7:52] How Quantum Computing Impacts Security

[11:22] The Mission Behind Project 11

[18:58] Harvest Now, Decrypt Later

[21:30] The Challenges of Post-Quantum Cryptography

[27:29] From Engineer to CTO

[30:20] A Practical Quantum Security Plan for CTOs

[36:56] Identifying Critical Systems and Data

[41:03] Taking the Easy Wins

[43:12] Preparing Vendors for the Quantum Transition

[43:57] Avoiding Migration Debt

[45:59] Building Crypto Agility

[48:35] The Quantum Challenge for IoT

[54:50] Preparing for Q Day

Find more from Conor Deegan and learn more about Project 11 through his professional channels: projecteleven.com, X, and LinkedIn. Conor welcomes direct contact on post-quantum migration questions.

Find more from Adam on https://www.linkedin.com/in/adamhorner/ and https://www.youtube.com/@TheCTOplaybook, and explore coaching, cohorts, and how you can stay up to date at the https://www.thectoplaybook.com/, helping you build your own playbook for your path at your pace.

If you prefer listening, check out the podcast at https://bit.ly/thectoplaybook.

Twenty years at one company should have made my guest predictable, and that is exactly the trap he built a method to escape.

Build your own CTO Playbook at www.theCTOplaybook.com - the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

Krystian Kolondra runs the browser portfolio at Opera, a set of products used by more than 300 million people, and he has spent two decades watching what happens to technical judgment when the work that built it starts to disappear.

His argument is uncomfortable. Your judgment was earned through execution, and AI compresses execution. So what is training your judgment now?

His answer starts somewhere I did not expect: an audit of the questions you stop asking. He calls it the shape of your curiosity, and he thinks most leaders have never looked at theirs.

We get into why he refuses to check AI's output and flips the task back on the model instead. There is a sniper analogy that reframes what "learning from failure" actually requires. There is a story about Opera GX where the first questions his team asked turned out to be the wrong ones, and the right ones only appeared once they went somewhere uncomfortable.

If you lead engineers, and you have felt the ground shift under what "senior judgment" means, this conversation gives you something concrete to practice tomorrow morning, before your first coffee.

You'll Learn:

[0:00] Introduction

[5:46] Twenty years at Opera and the business behind free browsers

[7:18] Why leading five hundred people is a different job entirely

[9:39] The temptation to stick with what already worked

[12:39] Curiosity as a counterweight to accumulated experience

[17:44] How the gamer browser came from asking the wrong questions

[23:26] Learning from failure means planning the shot before firing

[29:06] How AI compresses execution but never the judgment

[36:14] Auditing the questions you never ask to find blind spots

Resources Mentioned:

A Beautiful Constraint by Adam Morgan and Mark Barden | Book

Theory of Constraints by Eliyahu M. Goldratt | Book

Really Achieving Your Childhood Dreams (aka The Last Lecture) with Randy Pausch | TED Talk

Eric Ries, Validated Learning | Wikipedia

Igor Grossmann | Website

A route to well-being: intelligence versus wise reasoning by Grossmann, I. et al. | Article

Unlock more ways to make your browser yours with deeper personalization and an expanded modding universe with the Opera GX Gaming Browser here.

Find more from Krystian Kolondra on LinkedIn.

Find more from Adam on LinkedIn and YouTube, and explore coaching, cohorts, and how you can stay up to date at theCTOplaybook.com, helping you build your own playbook for your path at your pace.

Most CTOs think adopting AI means picking the right tools, but Sumeet argues the real work is closing the accountability gap between agents and engineers.

Build your own CTO Playbook at www.theCTOplaybook.com - the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

My guest this week is Sumeet Vaidya, co-founder and CEO of Crafting, who spent years at Facebook, Uber, and Discord before building the infrastructure enterprise engineering teams now use to put agents to work safely.

His perspective matters because he sees what CTOs say publicly, and what their teams are actually doing internally, and the gap is bigger than most leaders want to admit.

Roughly 80% of enterprise engineering orgs, he estimates, are doing close to nothing meaningful with AI. The rest are either reacting to hype or trying to build something durable, and the difference is not the tools they pick.

One partner running 3,000 agents against 250 engineers changed how he thinks about capacity entirely. That number is where his six diagnostic questions come from and where the cracks appear first: brittle CI/CD, accountability gaps for agents, and a hiring pattern that's storing up debt for the next downturn.

If you're under pressure to show AI gains while cutting token spend, this one is aimed at you.

You'll Learn:

[0:00] Introduction

[2:25] The disconnect between what companies say and what teams do

[4:38] Why 80% of enterprises watch and wait on agents

[9:31] Chasing the latest model as a vanity metric

[14:14] Resilient cultures survive shocks that short-term thinking cannot

[15:46] The engineering cultures that shaped Sumeet's career

[28:51] Silicon Valley is not the whole world

[33:02] What breaks if you 10X the team today

[42:31] Hiring only senior engineers stores up hidden debt

[47:11] Turning engineers from code writers into builders

Find more from Sumeet Vaidya on LinkedIn, X, or explore the Crafting Website.

Find more from Adam on LinkedIn and YouTube, and explore coaching, cohorts, and how you can stay up to date at theCTOplaybook.com, helping you build your own playbook for your path at your pace.

Ask a CTO which executive relationship matters most, and they'll name the CEO, but my guest today would send you to the CFO instead.

Build your own CTO Playbook at www.theCTOplaybook.com - the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

Anoop Tripathi is 32 years into a career that spans from writing firmware for telephone exchanges to sitting as CTO at Sauce Labs, and he's unusually direct about what decides whether your technical work gets funded.

Most of us step into this role believing the job is to be the strongest technologist in the building. Anoop thinks that's the fastest way to build an organisation of people waiting for commandments from the top.

The conversation gets sharper when he talks about scaling an engineering team from zero to over 100, then having to bring it back down to 70 in a tough market. His lessons from that period are not the ones I expected.

There's also a serious defence of laziness as an engineering virtue, a story about resellers walking out of a Citrix demo because the product was too good, and a framework he calls SHAPE that he uses to run his own career.

Fear is the dominant feeling across my coaching practice right now. Anoop's take on what that fear is costing tech leaders is worth contemplating.

You'll Learn:

[0:00] Introduction

[2:51] The double-edged sword of laziness as an engineering trait

[7:18] Reframing tech debt as a business outcome your CEO hears

[10:24] How investor pressure and a faster POC upended the Citrix integration story

[18:01] What he learned cutting a team from 100 to 70

[26:19] Spotting an inflection point before hindsight makes it obvious

[29:50] Fear you plant in your head tends to come true

[32:52] The SHAPE framework for tech leadership

[47:44] One piece of advice for a first-time CTO

Resources Mentioned:

Sauce Labs | Website

Find more from Anoop Tripathi on LinkedIn.

Find more from Adam on LinkedIn and YouTube, and explore coaching, cohorts, and how you can stay up to date at theCTOplaybook.com, helping you build your own playbook for your path at your pace.

I nearly turned my own summer into the cautionary tale. The thing that saved me was a last-minute week in the Swedish mountains.

Build your own CTO Playbook at www.theCTOplaybook.com - the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

I came home with a small number of clear thoughts about my business that I couldn't have produced at my desk, and they fade fast once you're back. This episode is for anyone heading back into work in the next fortnight, because how we come back in late August shapes the rest of the year more than almost anything else in September.

Real intent goes in on day one. The stack gets attacked head on. Two or three weeks later, we're extremely busy and have moved nothing forward.

There's a spaceflight analogy in here about why the most dangerous part of any return is the descent, and the one small decision that sets the angle for everything after it. Most of us come in too steep, and the queue gets to decide what matters so we don't have to. The cost is specific: the decisions only you could have made never surface, because they're never the loudest thing in the queue. There's a practical exercise at the end.

You'll Learn:

[0:00] Introduction

[2:05] The summer move that nearly made me the cautionary tale

[4:16] How spacecraft reentry explains a bad return to work

[6:52] Why late August beats January for real resolutions

[9:36] Career trains every leader to choose the steep angle

[10:52] Heavy first week is the friction doing its job

[12:37] Naming your own re-entry angle before the queue decides

[17:14] A controlled re-entry playbook in four steps

Find more from Adam on LinkedIn and YouTube, and explore coaching, cohorts, and how you can stay up to date at theCTOplaybook.com, helping you build your own playbook for your path at your pace.

One CTO spent two weeks tearing frontier models out and ended up back where they started. Another spent a few days building a tested escape route for what actually mattered.

Build your own CTO Playbook at www.theCTOplaybook.com - the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

The safe CTOs aren't the ones who trust their AI provider, and they aren't the ones tearing frontier models out of every workflow; they're the ones who've triaged their dependencies and tested a real escape route for the handful that matter. I'm walking through this one solo because it's a structural risk most engineering leaders felt this year but haven't priced.



Frontier AI is a supplier you can't audit, and it can be switched off by forces sitting outside your contract. Your invoices can be paid in full, and the capability can still vanish.

Most of us onboarded this dependency without ever writing it down. It spread the way water finds its level, and it became load-bearing before anyone noticed. Where in your stack would a frontier model going dark tomorrow actually hurt, not annoy you, but hurt?

The third option is triage: trust the dependency where stakes are low, temper it where you're more reliant than is wise, and override it with a tested alternative for the workflows that would cost you the business.

Two CTOs I work with responded to the same news in almost opposite ways, and only one of them still has a working escape route.

You'll Learn:

[0:00] Introduction

[2:06] What actually happened when two frontier AI models went dark for eighteen days

[4:26] Why a frontier AI model is a supplier you can't audit or control

[6:37] The dependency most teams priced at zero and never wrote down

[8:41] The fire drill that went off for someone else's building

[11:39] Two CTOs face the same news and respond in almost opposite ways

[15:02] Where in your stack would a model going dark tomorrow actually hurt?

[19:27] Why an untested escape route is just a hope, not a fallback

Find more from Adam on LinkedIn and YouTube, and explore coaching, cohorts, and how you can stay up to date at theCTOplaybook.com, helping you build your own playbook for your path at your pace.

A founder went from zero to $10 million ARR in six months with six engineers and fifty agents, and the gap between idea and shipped feature was nine hours.

Build your own CTO Playbook at www.theCTOplaybook.com - the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

Most CTOs think going deeper on the tech is how they win this AI wave. My guest this week thinks that instinct is working against them.

Nat (Rajesh) Natarajan is COO at G-P and has had a front row seat to every major platform shift since he helped found Travelocity in 1995, which is why his read on this moment carries weight.

He's convinced this shift is bigger than the internet, mobile, and cloud combined. But the CTOs who pull ahead won't be the ones with the deepest technical mastery. They'll be the ones who understand the business well enough to know which problems are actually worth solving.

Nat shared a story about a founder who went from zero to $10 million ARR in six months, with six engineers and roughly fifty agents doing the work. The gap between a customer idea and shipped capability was nine hours.

That's the world CTOs are now leading in. Technical skills are becoming a commodity. Critical thinking, business judgment, and knowing what the CEO actually needs from you are not.

If you're a CTO trying to figure out where to stand in this shift, start here.

You'll Learn:

[0:00] Introduction

[3:06] How early machine learning at PayPal and Ancestry taught Nat what AI is really for

[9:43] Why this AI shift is bigger than internet, mobile, and cloud combined

[11:06] The 5% who see the future, and why they're rarely your best engineers

[15:34] Why a 1976 prediction about paper currency should humble every AI evangelist

[17:50] Six engineers, 50 agents, $10 million ARR in six months

[23:32] Why only the CEO can actually make a company AI-first

[32:12] Unreasonably reasonable: how Nat pushes teams toward goals they won't fully hit

[35:54] The guardrails problem most agentic solutions are ignoring

[42:32] Why technical skills are now a commodity and critical thinking is the North Star

To learn more about G-P and its agentic AI innovation, please visit g-p.com.

If you’re on LinkedIn, be sure to follow G-P there too.

Find more from Adam on LinkedIn and YouTube, and explore coaching, cohorts, and how you can stay up to date at theCTOplaybook.com, helping you build your own playbook for your path at your pace.

Shielding your team from context isn't protection, it's a subtle underestimation of the people you hired for their judgment.

Build your own CTO Playbook at www.theCTOplaybook.com - the leadership platform built for the full CTO journey. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact.

The thing most technical leaders are proudest of is the same thing slowly cracking them in half. I coach CTOs through this exact pattern, and the reaction is almost always surprise rather than recognition.

You take the hits. You field the awkward questions. You sit in the meeting that would have derailed the sprint, and walk out telling the team not to worry about it. It feels like leadership. It feels like care.

Then I ask one question and the whole frame shifts. What if the way you're protecting your team is what's keeping them small?

This episode is about the structure you've turned yourself into without ever deciding to... The dam versus the firewall. Why one of them is exhausting, rigid, and impossible to sustain, and the other changes your job in a world where AI has collapsed the cost of reaching raw context.

If you've ever felt the strain of holding everything together, the discomfort you feel listening to this isn't a problem. It's the start of doing it differently.

You’ll Learn:

[0:00] Introduction

[3:58] The dam metaphor and why no human was built to hold that much back

[5:58] The instinct nobody trained into us, and why it operates below examination

[8:08] From dam to firewall, a model every technical leader already understands

[10:55] Why filtering for your team robs them of the reps they need to grow

[13:36] The belief hiding under the dam, that they can't handle the truth

[15:46] How AI collapsed the last practical argument for the leader as the filter

[18:14] The four postures for standing in the stream without becoming the wall

[20:39] Choosing which structure you become in relation to the stream

Find more from Adam on LinkedIn and YouTube, and explore coaching, cohorts, and how you can stay up to date at theCTOplaybook.com, helping you build your own playbook for your path at your pace.