Steve Taplin “sells” engineers for a living.But lately, he’s telling most of his clients the same thing…

“You don’t need more of them.”

Taplin runs a software engineering firm. His clients call him when something is broken — seven years into a roadmap, nothing shipping…

Much of the time, it’s a CEO who wants to know what’s wrong with the engineering team. In more and more cases, after spending a few days on site, he ends up giving them the same answer: engineering isn’t the problem.

That is an unusual thing to say when you sell engineering capacity for a living.

But that’s what makes Steve so trustworthy.

If engineering isn’t the problem — then what is?

Here’s how Steve puts it: Engineering is the only function that really adopted AI. The coding assistants work. Teams are faster. The gains are real.

But shipping software takes two things: deciding what to build, and building it. AI made the building fast. It did nothing for the deciding.

The bottleneck didn’t go away. It just moved upstream, to product.

I’ve seen this in my own work: a CTO runs a genuinely good AI program. Then their engineers run out of things to do. Velocity gains only make a difference when there’s a next thing in the queue.

It’s easy to clock a review queue back up. But no one’s measuring when a product team fails to make a decision.

Product may need to catch up. But there’s still plenty of opportunity for Engineering to push forward.

We discuss what those opportunities are and how to execute on them — including these three items that are worth getting started on this week:

* Review your backlog. When Steve ran this exercise with a recent client, the measure that came back was 14 years. That number is not an engineering capacity problem…we discuss what it is and what comes next.

* Cap how much of your code AI writes. With insider access across dozens of organizations, Steve’s found there is something of a “magic number.”

* Flip how you train engineers on AI. For the vast majority of teams, AI writes code → Engineer tests it. Steve’s teams flip it. And get consistently better results.

Also in the full episode:

* Why I don’t believe AI costs are ever coming down. We were sold this exact promise fifteen years ago and it wasn’t true then either.

* The tech debt surprise: why some engineering leaders are suddenly less worried about their legacy code than they were a year ago.

And then there’s the central debate — and I want you guys to weigh in on it…

Steve says product is starved. It never got the tools engineering got. He wants the person closest to the customer writing the requirements.

I think the opposite. In a $100M company, most of product management is coordination. AI now writes the requirements and builds the prototypes. What’s left of the job is talking to customers and picking which problems to solve…let engineering absorb the rest.

Tell me what you guys think — settle it for us.

Steve and I have gotten into it before. Hear another heated conversation — this time about CTO Leadership - on the Software Leaders Uncensored Podcast.

Steve, thanks for joining me.

This was a particularly fun conversation — and I suspect we’ll be revisiting some of these core discussion points as things unfold.

About Steve

Steve Taplin is founder and CEO of Sonatafy Technology, a software delivery firm with engineering teams across the US and Latin America, which means he spends most of his working life inside other companies’ delivery problems.

He is co-author of The Backlog Illusion, written with Sonatafy CTO Chris Horvat, and author of Fail Hard, Win Big.

Steve also hosts Software Leaders Uncensored — a podcast that features close to 200 conversations with CIOs, CTOs and engineering leaders.

Connect with Steve on LinkedIn: linkedin.com/in/stevetaplin

In this episode of Technocratic, I speak with Tracy Edwards, a highly experienced product and technology executive, about the messy reality of aligning product, engineering, and business outcomes in the AI era.

The conversation starts with a problem almost every technology organization knows too well: teams are busy, roadmaps are full, JIRA is active, velocity charts look impressive — but the business is still asking, “Why didn’t you move the needle?”

Tracy brings a practical operator’s view to the conversation, breaking down why engineering and product so often drift apart, why too many OKRs create noise instead of focus, and why roadmap capacity has to be treated like a strategic asset.

She also shares a simple but powerful way to think about engineering investment, greenfield innovation, optimization, and maintenance.

We also discuss AI’s impact on product & engineering, where Tracy cuts through the hype with a line every executive should hear: AI is not a strategy. It is a tool.

We explore why faster coding does not automatically mean better product output, why companies are struggling to measure AI productivity, and why the real breakthrough may not be “doing the same work faster,” but redesigning entire workflows.

We also get into taste, judgment, and why product leaders still need to know which problems are worth solving in the first place.

I REALLY liked this episode, Tracy was so on the money with everything, and I think you’ll enjoy it.

—Bobby 🇺🇸

Quick Links

You can find more on Tracy at LinkedIn:

* https://www.linkedin.com/in/edwardstracy/

And more on Bobby here:

* https://www.linkedin.com/in/bobby-tahir/

If you want to be a guest on Technoratic or host Bobby on your podcast, please contact us at: [email protected]

Chanille Juneau oversees product, engineering, security, DevOps, and QA for Accumulus, a platform that exchanges confidential data between the world’s largest drug makers and regulators in 75+ countries. She’s never written a line of code. It doesn’t matter.

The CPTO Role and AI Reward the Same Thing: Systems-Level Thinking

A great CPTO doesn’t need dev experience. Chanille Juneau proves that.

CPTO is a systems role. The value comes from connecting domains rather than mastering any one of them: seeing how a product bet hits infrastructure cost, how an architecture call lands on support, how a security rule reshapes the roadmap. The hard problems live in those seams, and only someone holding the whole picture catches them in time.

Chanille points out how AI works the same way. If you want it to do something really well, it needs all the context. Feed it a thin slice and it produces confident work that doesn’t fit the system around it. The role and the technology reward the same scarce thing: whole-system context.

You Only Move as Fast as Your Slowest Stage

Engineering velocity is up 30, 50, 80 percent, and the roadmap still hasn’t moved. I hear it from CTOs constantly. The ship rate’s the same, but the bill’s getting bigger.

Shipping is a chain: requirements, story, code, QA, deploy, security, prod. You move only as fast as the slowest stage. Speed engineering up 80 percent while security sits at 20 - the whole system gains 20. The bottleneck just moves downstream.

So the fix is rebalancing the chain: add QA and security capacity where the work piles up, even if that means fewer engineers. More engineering seats won’t move it; that stage was probably never your constraint. AI ROI is a throughput problem.

Judgment Is the Job Now

AI amplifies whatever direction you give it. Point it at a sharp problem with clear context and it gets you somewhere good - fast. Point it at a vague one and it gets you somewhere bad just as quickly. You can’t hand it your product strategy and walk away. When AI can produce any output, the scarce input is judgment: knowing which problem to point it at, and why.

AI does junior-level work well, which tempts leaders to keep only seniors. Push that across the industry and you starve the pipeline that grows seniors in the first place. Judgment is the one thing you can’t automate, and cutting the junior ranks is how you stop building it.

In the episode:

* How to put AI features in front of customers who legally can’t switch them on yet, without slowing the sale or the rollout.

* Why running out of AI credits actually points to success right now - and whether that bill ever comes back down.

* Why I think AI is making the hardest job in tech harder, not easier: three mandates landing on one person.

I’ve done this job. It’s the hardest in tech short of the CISO’s, and AI is making it harder. Chanille makes it seem doable, nonetheless.

Listen to the full episode to find out how.

About Chanille Juneau

Chanille Juneau is Chief Product and Technology Officer at Accumulus Technologies, the company spun out in 2025 to commercialize a platform that lets life sciences companies exchange data and submissions with regulators across 75+ countries in real time. She was previously Chief Technology Officer at Berry Appleman & Leiden, where the software her team built helped land the firm on Fast Company’s 2023 World’s Most Innovative Companies list. She came up through product, not engineering, and holds a degree in biomedical science she now puts to work in the industry she helps run.

Connect with Chanille: linkedin.com/in/chanille

Gabi Bufrem has been on every side of product leadership — IC, CPO, and now the person other product leaders call when the job stops making sense.

The Shift Nobody Teaches You

There’s a version of this conversation that’s comfortable — the one where we talk about leadership development in broad strokes and everybody nods along. Gabi Bufrem doesn’t do that version.

She goes straight at something most technology leaders have felt but rarely say out loud: the things that got you promoted are actively working against you now. You were the best problem solver. The most technically fluent. The one who caught the detail everyone else missed. And now every one of those instincts is pulling you toward the wrong work.

The day you got the title, the job changed. You’re responsible for how well your people perform, not for performing yourself. Bad leaders assume it’s as easy done as it is said. To avoid learning the hard way, recognition is key.

Gabi outlines some of the critical ways former strengths get inverted when you move up:

* Being obsessed with the intricacies of the work made you sharp. As a leader, it’s micromanaging.

* Solving every problem that hit your desk made you indispensable. Now it’s suffocating your team.

* Knowing everything happening across your product made you reliable. At scale, it’s impossible. Probably wasting your time.

* Having strong convictions about how work should be done made you a top performer. Forcing your operating style on everyone else makes you unpopular.

Reconstructing How You Operate

Seeing the pattern is one thing but breaking it is where most leaders get stuck. Oftentimes, the problem compounds before it gets better.

Take micromanaging, for example. It’s something almost every former IC struggles with and almost none of them even know they’re doing it. Her definition is precise: micromanaging is focusing on the work instead of the person. The CTO who reviews a deck to flag that something is “0.5 off” is focused on the work. The one who asks why their PM struggled to defend a position in that meeting is focused on the person.

Calling out every mistake is tedious. Coaching someone to think differently about how they present, execute, operate — is meaningful. The question to ask before you intervene: am I fixing the output, or am I developing the person?

Sometimes Owning Your Title is Half the Battle

Gabi makes an observation that resonates with me: a lot of leaders don’t feel they have standing to develop their people in the first place. Redefining the dynamic is one of the most awkward parts of the process: how do you delegate to people who were once your peers? Who are you to tell them how to do their job?

I struggled with this when I first stepped into a leadership role. But Gabi reframes it powerfully: leading people is not a right. It’s a responsibility. It was written into the job before anyone handed you the title.

And there’s a ceiling to what even the most self-aware leaders can fix alone. Gabi only understood that after stepping out of the role entirely. I was a CTO for 10+ years before someone got me to invest in external perspective. I was self-critical, held myself to high standards. I didn’t think I needed someone else to check me. Years later, I only wish I’d made the investment sooner.

Listen for:

* How to tell a high performer they need to level up for the next stage of growth without crushing their confidence

* The one-on-one debate: keep them, cut them, or fix what’s actually broken

* Two habits any executive can start this week: one about trust, one about impulse control.

Huge thanks to Gabi for a conversation that made me rethink my whole concept of the job at the leadership level.

About Gabi: Gabrielle Bufrem coaches product leaders and founders after spending years doing the job herself, at Google and across nine industries on three continents. She lives in New York City and speaks four languages.

Learn more about Gabi: gabriellebufrem.com

Connect with Gabi on LinkedIn: linkedin.com/in/gabriellebufrem/

The Product Leader Who Stopped Blaming Silicon Valley

If you’ve spent any time in product management over the last decade, you’ve probably felt it — that slow creep of the role becoming narrower, more reactive, more about managing a backlog than shaping a business. Most people in the field have a villain for that story: the tech-first founder, the engineering-obsessed CTO, Silicon Valley’s build-and-ship culture.

Grant Elliott had that villain, too. He even wrote about it — a piece called Did Silicon Valley Kill Product Management? Then he spent a year talking to CPOs, CEOs, and product leaders across the industry and arrived at an answer he wasn’t expecting: Silicon Valley didn’t kill product management. Product surrendered it. Gradually, and in many cases without much of a fight.

That reframe — from external blame to internal accountability — is what makes Grant’s perspective so compelling. He’s not here to commiserate. He’s here to make the case that the conditions to fix it have never been better, and that the window to act is open right now.

AI Didn’t Create the Problem — But It Can Help Fix It

Grant opens by doing something most guests don’t: he lowers the temperature on AI. He’s seen this movie before — internet, cloud, mobile — and the mistake is always the same: companies chase the technology instead of the problem. And right now, he’s watching CEOs set wildly ambitious efficiency targets based on a version of AI that doesn’t exist yet.

But the more interesting argument isn’t about AI hype. It’s about a structural imbalance that’s been building for twenty years between how fast companies can build and how well they understand what to build — and why AI might be the first thing capable of correcting it. Grant lays out exactly how that imbalance developed, why it’s made product the weaker function in most orgs, and what has to change for AI to actually fix it rather than make it worse.

He also has a sharp take on the whole “product-led vs. engineering-led” debate that I think will resonate with anyone who’s tired of that framing — I know I am. I jumped in with my own experience on this one, and we ended up in strong agreement about exactly what — and how long — it takes to achieve true product/engineering balance at a $100 million company.

Listen to the full episode for the frameworks, diagnostics, and case studies:

* Grant’s direct challenge to anyone in a product role today who feels like they don’t have enough authority — and why he thinks it has to be as much bottom-up ownership as top-down support.

* The electronic signature story — a real roadmap being driven by the wrong customers entirely, and what proper discovery would have caught before a single line of code was written.

* The two traits Grant considers non-negotiable in every great product hire — and the AT&T story that shows exactly what one of them looks like in practice

* What to look for in a job description that tells you immediately whether a company actually understands product — and the one reporting structure that’s a dealbreaker every time

Huge thanks to Grant for a conversation that’s as honest as it is practical. This one’s for anyone who’s ever felt the product role shrinking around them — and wants to know whether it has to.

About Grant

Grant Elliott, Co-Founder and CEO of SimplAI Product, has spent years obsessing over how to apply AI to strengthen how product organizations operate. He brings 30 years in product and technology leadership, including running a $500M global product portfolio as Product Director at AT&T, co-founding and leading Ostendio (a venture-backed SaaS company), and teaching entrepreneurship and business strategy at Pratt Institute.

Originally from Scotland, Grant is on what he calls a mission to make product management cool again.

Learn more about Grant and his background: linkedin.com/in/grantelliott.

The Seasoned Tech Leader Who Went Back to School — And What He Learned

If you look at Chris Bunk’s résumé, you’ll see an undeniably impressive track record. Current CPTO at FastSpring. SVP of Engineering at WorldPay. Years of leadership across fintech and enterprise technology. His is the kind of career arc that earns you a seat at the table…without having to prove much of anything anymore.

But there’s one entry that stands apart. One you probably won’t find on even the most decorated technology leader’s CV: a one-year sabbatical devoted to AI learning and discovery.

In 2024, Chris took something like a gap year. Not to decompress, not to consult — but to go fully hands-on with AI development tools because he believed the moment was simply too significant to experience from a distance. Cursor. Windsurf. Eventually, Claude Code. He went deep, built things, joined communities of like-minded senior technologists doing the same, and emerged with something most CTOs at his level don’t have: genuine, practitioner-level fluency in agentic development.

That decision — and everything that came out of it — makes my conversation with Chris a particularly fascinating one.

A framework that actually holds: the 3-bucket model for AI value creation.

Chris doesn’t talk about AI the way a lot of executives do. He comes in with a framework — one that’s clean, compelling, and consistent throughout.

* Bucket 1: Agentic Development — Using AI to make product and engineering faster, better, and cheaper. This is the bucket accelerating fastest right now, and in Chris’s view, still in its infancy.

* Bucket 2: Enterprise Operationalization — Making every function across the company more efficient — not just engineering, but ops, finance, support, and beyond.

* Bucket 3: Net-New Product Capabilities — The things that simply weren’t possible before. New value delivered to customers that couldn’t have existed without AI.

Chris’s argument: most organizations are still dabbling at the edges of all three. They checked a box with a chatbot, deployed a copilot even. But they haven’t gone deep. That gap — the one between the checkbox and the real value — that’s where the opportunity lives.

What actually changes when you build with AI at scale.

Now at FastSpring, Chris is running the change management playbook in real time. Three things stand out from his experience in the trenches.

* Context window management is the skill. Output degradation — and all the developer frustration it brings — isn’t just about model quality. It’s about what you feed the AI. Careful context window management really matters.

* Vibe coding and agentic development are not the same thing. Vibe coding is fast and disposable: great for prototypes. Agentic development is deliberate: intentional architecture, upfront documentation, guardrails. Shipping something you can maintain, scale, and trust is a different discipline entirely.

* Trust is earned incrementally. Like getting comfortable with Tesla’s full self-driving — you start skeptical, hovering over the wheel, until the technology earns enough confidence that you trust it more than your own instincts.

The sabbatical, the framework, the craft of building with AI at scale — the foundation, but not the whole story.

In the full episode, you’ll hear Chris’s thoughts on the human cost of this shift on engineering teams, why he believes one entire product role is already functionally dead, and what his own AI-powered personal operating system actually looks like running day-to-day.

We cover:

* Why Chris thinks the engineers most at risk from AI aren’t the ones you’d expect — and which profile is about to have the best run of their career.

* Building BunkOS: Chris’s own AI-powered Chief of Staff that knows his personality profiles, manages his calendar, and holds him accountable — and why he thinks the real barrier to building one isn’t technical ability, it’s the willingness to do it.

* Why Chris, a self-described 98% believer in AGI happening in our lifetimes, recently caught himself wondering if it might already be here — and what specifically made him pause.

Chris, thank you.

This one stuck with me.

More About Chris: Chris Bunk is a technology executive with 20+ years of experience scaling teams and leading transformation in FinTech, SaaS, and PayTech. He has expertise in organizational design, AI-driven innovation, and full product lifecycle management. Chris has scaled engineering and product orgs from 15 to 200+ through to successful acquisition, driven 100% YoY growth in transactional volume and $2B+ in monthly transactions, and spearheaded organizational redesign for 300+ employees.

A very Merry Christmas 🎅 to all those who celebrate!

Two holiday gifts for you today:

1st 🎁 is an in-depth conversation with Susanna Holt (CTO) who offers us a great set of tips and tricks on effectively leveraging AI in Engineering & the broader organization.

2nd 🎁 is free access to our new Technical Debt Management Tool: Tech Debtonator which uses AI to take the pain out of dealing with Tech Debt.

Enjoy both in good health & great spirits :)

Happy Holidays!Bobby

More on Susanna

https://www.linkedin.com/in/susannaholt/

A customer-focused software engineering executive with nearly 30 years of experience, Susanna has led large-scale technology transformations across startups and global enterprises. Her expertise spans cloud migration, post-acquisition integration, organizational transformation, and scaling global teams, with a strong focus on applying GenAI to real business problems.

She is known for translating complex technical topics into clear, executive-level insights and has led teams of up to 500 across Asia, Europe, and North America. A high-EQ, inclusive leader, she brings a global perspective shaped by living and working across multiple countries and is also a Masters World Rowing Champion.

More on Technocratic

The Technocratic Podcast focuses on advice & wisdom from and for technology and product leaders.

For inquiries about appearing on or sponsoring the Technocratic podcast, please email [email protected]

I met Bethany Lyons by chance and it turns out that she is a semi-genius.

You don’t meet too many of those!

With a penchant for math + expertise in data & product leadership, Bethany has a unique vantage point on how Data Engineering & Quant Development is impacting Product & Engineering.

More on Bethany here: https://www.linkedin.com/in/bethany-lyons-0395aa74/

Bethany’s partner in crime Mayank Mohta also joined us to offer insight into what Data Engineers actually do (!) & their role in the engineering org. Myank is a Sr. Data Engineer & Product Manager.

More on Mayank here: https://www.linkedin.com/in/mayank-mohta/

Enjoy the episode!

Appearing on the Podcast

https://newsletter.technocratic.io/podcast

For inquiries about appearing on or sponsoring the Technocratic podcast, please email [email protected]—

And remember, keep the shark swimming! 🦈

Bobby

Bob Gilbreath is one of those rare people who is not only a multiple time, successful startup founder, but he also loves to share his knowledge with the world.

You can find him at: https://theworkaround.substack.com/

He decided to hang out with us here at Technocratic and we had a great time covering a ton of topics helpful to startup founders, including:

* Balancing Work & Life

* Impact on Family

* Choosing the Right Co-founders

* Raising Money

* Major Startup Lessons

* AI & It’s Impact

Really hope you enjoy this episode, I certainly did, and if you have any questions about startups please don’t hesitate to reach out at [email protected]

A Few More Links to Bob

https://www.linkedin.com/in/bobgilbreath/https://theworkaround.substack.com/https://fleetholdingco.com/

Appearing on Technocratic

https://newsletter.technocratic.io/podcast

For inquiries about appearing on or sponsoring the Technocratic podcast, please email [email protected]—

And remember, keep the shark swimming! 🦈

Bobby

Today, I analyze the topic of a GovTech business that released a new product to much fanfare, only to have it immediately crash due to too much traffic on the app, how they ultimately went about fixing the issue, and the lessons learned.

If you’d like to submit a topic for analysis feel free to reach out: [email protected]

And remember, keep the shark swimming! 🦈

Bobby