Matt Watson

Serial Entrepreneur, Startup CTO, Podcaster, Blogger @ VisionaryCTO.com

I’ve spent over 20 years as a founder and CTO, starting in 2003 when I co-founded one of the earliest SaaS companies. We bootstrapped VinSolutions to $35M ARR and sold it for $150M in 2011.

Since then, I’ve built and scaled multiple companies. I’ve led engineering teams through every phase of growth. I’ve also learned firsthand what breaks when leadership, product, and engineering aren’t aligned.

In 2018, I started Full Scale. It began as a way to help myself scale faster with top-tier engineering talent in the Philippines. Today, Full Scale has over 300 developers supporting fast-growing tech companies that need to move efficiently and with clarity.

I recently launched Full Scale Ventures, a venture studio focused on solving real problems for startup founders and engineering leaders. Our first product is EngagePath, a tool designed to help thought leaders identify warm leaders from their followers.

Most recently, I wrote Product Driven, a book for engineering leaders who want to stop playing whack-a-mole with tickets and start building real product momentum. The book is about aligning culture, leadership, and customer focus. Product thinking isn’t just for product managers anymore.

I also host a podcast and write a weekly newsletter focused on the gap between business and engineering and how to close it.

Read the book and subscribe here: https://productdriven.com/connect

Learn more about Full Scale: https://fullscale.io

Founder & CEO

We started Full Scale to do software development the right way from the Philippines. Our 300+ employees are helping dozens of companies build their software quickly and affordably.

* Philippines based, English fluency is superb!
* Staff augmentation model, our team works directly for you!
* Focused on senior level developers
* No middle man project managers in the way

Startups don’t need a CTO. They need a CPTO. That’s a hill I’m willing to die on.

For 20+ years, I’ve had the CTO title. The only reason I’ve been successful is because I never acted like just the head of engineering. I acted like the Chief Product AND Technology Officer.

Building software isn’t about code. It’s about outcomes.

It’s knowing what matters to the customer.
It’s tying what you build to real value.
It’s iterating based on impact, not ego.

In the age of AI, this is even more obvious.

AI can write the code. That’s not the bottleneck anymore.

Product thinking is.

What should we build?
Why does it matter?
How does it change the user’s life?

The worst setup in a startup is a Head of Product and a Head of Engineering negotiating priorities while momentum dies.

Engineering and product can’t be separate empires.

They have to be fused.

If you think you need to hire a CTO in 2026, they better be a CPTO.

5x Founder & CTO | Author of Product Driven | Bootstrapped to 9-Figure SaaS Exit | CEO of Full Scale | Teaching Product Thinking to Engineers

Realizations you have after being a CTO for 20+ years:

1. Speed doesn't come from coding faster.

It comes from clarity, focus, and fewer surprise pivots.

2. Most engineers want to care about the product.

They just don’t get the context they need to do it well.

3. Vision isn’t an HR slogan or a slide deck.

It’s a tactical tool that guides real-time decisions across the team. 

4. The best engineers aren’t just builders.

They’re problem solvers. But only if they understand the problem.

5. Bad culture isn't loud.

It’s passive. Quiet meetings. No pushback. Lots of guessing.

6. “Done” isn’t when it ships.

It’s when it solves something for the user.

7. Teams don’t magically become product driven.

They become product driven when leadership acts like it matters.

8. Product debt slows you down more than tech debt.

No amount of clean code can fix the wrong thing built really well.

9. Focus is a leadership decision.

Not a prioritization framework.

10. If your engineers are stuck waiting for better requirements...

You don’t need a new process. You need to lead differently.

Learn dozens more lessons from my new book, Product Driven. Also now available on Audible!

Scaling Tech Teams through Product Thinking & High-Performing Offshore Talent | CEO @ Full Scale | Author Product Driven | Podcast Host

AI generated code is amazing until the shit hits the fan.
Production is down. Your best engineer is troubleshooting it.

There is one massive problem.

They have never debugged any of this code before. They didn't write the code, AI did.

They lack familiarity and muscle memory about how the code works.

It isn't much different than asking your engineer who started yesterday to go figure it out.

This is the ultimate problem with AI generated systems.

They work great until they don't. Then nobody knows how it actually works. There is no deep expertise.

How do we fix this?

Pray that AI can fix it when production is down?

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Author

Chief Executive Officer

Podcast Host

Founder, CEO, CTO

Here are the 4 golden rules of software development: 
 
#1 If it can be null, it will be null 
 
#2 The answer to every question is "It depends" 
 
#3 Getting users to actually use software is 10x harder than building it 
 
#4 Great requirements are more important than great developers

If you can build an entire application with a few AI prompts, so can your competitors.

You have nothing of unique value.

Real software isn't that simple.

Every new software project should start with the database, not the UI. 
 
The database is the foundation of most software applications. 
 
Bad database architecture can make it difficult to add new features later. 
 
The UI is simply the lipstick on the pig if the database is done incorrectly! 
 
Spending time understanding the UI and UX is really important. It helps shape the database architecture. 
 
But when it comes time to write code and build something, it always starts with the database.

Most “senior” engineers aren’t senior.
They’re just juniors who stayed at the same company for 5+ years.

They know every quirk of the legacy code.
They can tell you which bug comes back every quarter.
But ask them to design a brand new clean, simple service? Well...

Because memorizing a codebase isn’t the same as mastering fundamentals.
Surviving time-in-seat isn’t the same as developing judgment.

At Full Scale, we don’t leave seniority up to tenure.
We provide ongoing training so our engineers develop the skills that actually make them seniors:

⚡ Architecture thinking. How to design systems that scale and stay simple.

⚡ Problem-solving. Approaching new challenges instead of defaulting to old hacks.

⚡ Communication. Explaining tradeoffs clearly to teammates and stakeholders.

⚡ Product context. Understanding why the code matters, not just how it works.


Real senior engineers aren’t defined by years in the seat.
They’re defined by how they grow and how they make the team better.

With 300+ engineers, career growth isn’t optional.
It is critical to our success and to the success of the 85 clients we serve at Full Scale.

I need to hire offshore software engineers for my startup. 
 
Where should I hire them? 
 
How do I trust them? 
 
How do I make this work?

Our client just fired two local software engineers.

Not because they were bad engineers.

Because they refused to use AI.

This isn’t the first time a profession has faced a shift like this.

Travel agents who ignored online booking tools disappeared.
Stockbrokers who ignored electronic trading platforms disappeared.
Accountants who ignored spreadsheet software disappeared.
Journalists who ignored the shift to digital media disappeared.

Technology doesn’t wait for people to get comfortable.

AI won’t replace engineers.

But engineers who use AI will replace engineers who don’t.

Claude Code now commits 4% of all GitHub public commits. 

That is insane!

Every week I keep telling all of our team members and clients how important it is for them to learn to use these tools.

If you don't adapt to them, you are going to be left behind.

If you use it for a day and don't see the immense value in it, you are already left behind.

But is still like herding cats to get 300 people to use them and nearly 100 of our clients on board as well.

There will always be early adopters and late adopters.

The AI hype isn't hype for us. Claude Code and agentic engineering has permanently changed how we work.

4% of all commits is wild.

The real number of commits that used AI in some way is substantially higher.

Software engineering has a K-shaped skill gap right now.

The engineers embracing agentic AI are on the top curve. 

The ones who aren't are on the bottom, and the distance between them is growing fast.

We talk to leaders at tech companies every day. The things I'm hearing:
"I haven't written code since November."
"I'm getting 5x more work done."

Six months ago, a lot of us thought this was hype. 

Nobody thinks that anymore.

You're either figuring it out, or you're in denial. 

There's not much middle ground left.

This weekend we had "Wellness Day" for all of our employees.
A day dedicated to them and their families to relax.

Swimming, yoga, food, music, and lots of other activities for kids of all ages.

No laptops or Slack notifications. Just family fun!

At Full Scale, we believe that team work is more than sprints. We also need to slow down once in a while, and when we do, something special happens.

I learned a long time ago that without your team, you don't have a business. We have to make time to recharge, reconnect, and remember why we do this in the first place.

Thanks to all the Full Scale team members who made it out this year. Next up is the summer party in May!

Full Scale

The death to software companies is rarely the engineering team. It's the product team. 
 
Any engineering problem can be solved with money. At worst, it's a funding or management problem, not engineering problem. 
 
Want to build a nuclear bomb or put humans on the moon? With enough money... no problem. 
 
However, money can't solve a terrible product vision or business model. 
 
As they say... fortune favors the bold. In this case, a bold product vision. All too often, the problem is the lack of product vision. 
 
Many companies struggle with what to build, how to differentiate, and their target market. 
 
They meander around in circles with no strong direction. 
 
The engineering team is left rudderless and often lost in refactoring, maintenance, technical debt, and architecture rewrite oblivion. 
 
They produce no real product value to the end users. Instead of building cool products, they are left to focus on building cool code. 
 
Customers don't buy cool code. They buy cool products. 
 
Cool products require a bold product vision and focus. A startup can raise all the money in the world but can't raise a product vision. 
 
That usually comes from the founders and product team. 
 
If you want your startup to succeed, a strong product vision is the most important piece.

They were right about software engineering being dead.
Just not in the way people think.

Software isn’t code.

It’s architecture, tradeoffs, and understanding the customer well enough to know what not to build, and connecting technical decisions to real business outcomes.

That isn't dead and it isn't going anywhere.

That has always been the job. Most people just forgot.

If you think software engineering is just about code, your career is dying.

Don’t tell me you’re using AI for software engineering if all you’re doing is opening ChatGPT.

That was impressive in 2024.

It felt like a superpowered Google search. You could research faster. Draft cleaner code. Spin up a small function without digging through Stack Overflow.

But that’s the old baseline.

If you’re an engineer in 2026 and you say you’re “using AI,” the question isn’t whether you’re chatting with a model. It’s whether you’re running an agent with real context.

We’re not asking AI how to do something anymore.

We’re telling it what to build.

There’s a difference.

A quick prompt has no idea how your system is architected. It doesn’t know your naming conventions, your constraints, your edge cases, your tech debt, or the trade-offs you’ve already made. It’s guessing from the outside.

An agent operating inside your environment is different. It understands your repo. It sees your patterns. It knows the rules you’ve defined. Tools like Claude Code or agent-based Codex setups can take detailed instructions, apply them across a codebase, and execute within the context of your actual system.

That’s not a better Google search.

It’s a hundred times more powerful.

If your team says they’re “using AI” and all they’re doing is pasting snippets into ChatGPT, that’s like saying you’ve modernized because you use Stack Overflow.

Helpful? Sure.

Transformational? Not even close.

The shift is from Q&A to delegation.

From isolated prompts to persistent context.

From “how do I write this function?” to “here’s the objective, here’s the codebase, here are the constraints, go execute.”

That requires more from you, not less.

You have to define standards. Provide context. Be explicit about architecture and intent. AI is only as good as the environment you plug it into.

The teams that win over the next few years won’t be the ones casually dabbling with prompts.

They’ll be the ones who build systems around agents. Be that Claude Code, Cursor, ChatGPT Code, etc.

Are we sure this isn't a software engineer running Claude Code?

BBC news showed this 15 screen setup.

“Monitoring the situation”
“Matrix”
“Where is my mouse?”

No matter what your thoughts on this. I genuinely impressed and feel ashamed of my four monitor setup now.


I’m off to buy more monitors 🤣

Shouldn't a "public benefit corporation" be against mass domestic surveillance and autonomous weapons?

I'm proud of Dario Amodei for holding his ground.

I wouldn't want my technology used for those things either.

CEO and Co-Founder at Anthropic

NoSQL will destroy your startup.
It's actually the #1 reason all startups fail.

Because you’re missing the most important thing every business is built on.

The foundation.
The core.
The one thing you cannot fake, skip, or outsource.

You’re scaling infrastructure.
Refining your stack.
Debating databases.

But none of that matters if you ignore this one truth.

And by the time you realize what’s missing, it’s already eating away at your runway.

Here’s the twist:

I’m not talking about databases.

I’m talking about your funnel.

No SQL = No Sales Qualified Leads.

You don’t need a better backend.
You need people to sell to.

Because you can’t scale a business that’s never had real demand.

And no tech stack in the world will save you from no leads.

You don't need rockstar developers. You need rockstar requirements.

That was true in 2025. It's even more true now.

AI can write the code. It can't write the requirements.

Most of us are building really simple software. Display some data,
save it to a database, call an API. AI handles that now. A junior dev
with good prompts ships features in hours.

AI can't figure out what the customer actually meant.
It can't tell you which features matter and which are distractions.
It can't make the call when scope gets messy and someone has to say no.

The requirements, the clarity, the "why this and not that": that's
still a human job. Now it's the only job that matters.

AI made average developers rockstars.
It made bad requirements catastrophic.

"I used to think the code was the thing."

The most code-obsessed architect I know told me that yesterday.

Now he's watching four Claude Code agents churn out features while he figures out what to actually build next.

Code was never the thing.
What the code does is the thing.

Tech founders figured this out the hard way.
You can't sell code. You sell what the code makes possible.

CTO | Author of Engineering Leadership newsletter (65k+ subscribers) - Helping you become a great engineering leader!

I am an engineering leader with a passion for solving complicated problems with innovative concepts. I love learning about new technologies, sharing my knowledge and teaching others. 

I have a proven record of successfully managing Agile teams, setting the correct direction and culture as well as creating right processes and continuously improving them. I have overseen hiring and building up the teams as well as have strong emphasis on coaching and mentoring to direct reports.

You can reach me at info@gregorojstersek.com.

Gregor Ojstersek

Software engineering is becoming position-less.

For years, our industry relied on clearly defined roles: frontend, backend, QA, DevOps, manager, and architect.

Titles were clear, and responsibilities (depending on the company) have been decently defined.

But that clarity is now fading.

Today, companies care less about titles and more about the impact.

- Engineers are expected to think about the product and business.
- Managers are expected to be more hands-on.
- Architect, EM, and Staff Engineer roles are getting closer together.

The boundaries are blurring more and more.

With better tools, AI-assisted engineering, and smaller teams, versatility is becoming more and more expected.

Learn more in the latest Engineering Leadership article: https://lnkd.in/dg3yh8AR

CTO | Founder of Engineering Leadership newsletter (183k+ subscribers) - Helping you become a great engineering leader!

Saying "I don't know" is a sign of seniority for me.

It’s impossible to know all of the things, especially in Software Engineering. So not knowing something is no issue at all.

On the contrary, it shows me that you are self-aware and confident in yourself by saying that.

The same is true with admitting mistakes. It's a lot better when you acknowledge that you've been wrong as soon as you know.

And not try to hide it and hope that nobody will find out.

Nobody is perfect, we all make mistakes. Admitting them builds trust.

Learn more in this Engineering Leadership article: https://lnkd.in/erdeDUXM

CTO | Founder of Engineering Leadership newsletter (184k+ subscribers) - Helping you become a great engineering leader!

Advice for engineering leaders building AI-native engineering teams.

1. Don't focus too much on code generation and metrics like lines of code produced by AI

Instead, look for the real bottlenecks, as more code won’t solve the problem. The bottleneck might be code review, planning, or system design, especially as engineers become much more productive.

2. Let engineers experiment

Give teams a generous, sometimes unlimited, token budget and the freedom to experiment. Encourage people to explore without pressure or fixed expectations about what success should look like.

Where this breaks down is when leadership has a very rigid idea of how AI should be used and forces that approach onto teams.

3. Look for people who are excited about AI

Do you already have people who are excited about AI and noticeably more productive? If so, help them spread what they’re learning.

Create space for sharing: internal hackathons, demo sessions, and dedicated channels where people can exchange tips and workflows.

4. Stay close to technical details

As a leader, it’s more important than ever to stay close to the technical details. At the same time, it’s critical to empower your team to make decisions.

That means optimizing how decisions get made: clear ownership, clear escalation paths, and minimal friction.

To learn more, read this Engineering Leadership article: https://lnkd.in/d5ambTDW

Just asking for a friend :)

Happy Friday and have a great weekend!

First, build the RIGHT things. Second, build the things RIGHT.

It can be the difference between a successful and an unsuccessful project.

You can do the best work in the world:
- write the simplest code,
- add all the tests,
- create all the documentation.

But if you are building the wrong things for the users, it won't be perceived this way.

To learn more, read this Engineering Leadership article: https://lnkd.in/eJgJpbmS

I can tell a lot about engineer's seniority by the way they approach solving problems.

Here are 5 indicators that tell me if the engineer has a Senior mindset:

1. They ask all the right questions before they start solving the problem.

2. They REALLY take care of the problem, not just try to quickly patch it and leave it for others to deal with.

3. They focus on simplifying the solution as much as possible.

4. They know when the solution is good enough or when to optimize further.

5. They are self-aware and self-confident enough to admit when they don't know something.

To learn more, read this Engineering Leadership article: https://lnkd.in/eSHCwymt

Software engineering roles are getting closer together.

This is what I am seeing happening across the industry:

1. EM, Staff Engineer, and Architect roles are getting closer together

For middle managers, e.g., (Sr.) Engineering Managers, it’s becoming more and more common to be expected to take on Staff Engineer roles if needed, and Staff+ Engineers taking on manager hats if needed.

And the same for Architects as well → taking on a Staff Engineer role or manager role if needed.

2. Similar trend for PMs and Engineers

The role “Product Engineer” is getting more and more popular, and being product/business-minded is just going to be more and more important for all engineering roles.

Good examples are companies like OpenAI and Lovable, both of which look for their engineers to own projects end-to-end: the what, why, when, and also the how.

To learn more, read this Engineering Leadership article: https://lnkd.in/dg3yh8AR

Waiting for some pokemon AI tools to put it in the list :)

Happy Friday and have a great weekend!

First, build the RIGHT things. Second, build the things RIGHT.

This is more important than ever.

AI makes output cheap, impact becomes rarer. So, anyone can "ship more code", while business impact becomes that much more important.

And that's where the focus on building the RIGHT things is crucial.

To learn how software engineering productivity has evolved over the years, read the latest Engineering Leadership article we did together with Francisco Manuel Soto Ramírez here: https://lnkd.in/d9KqCUPs

AI tools are the onboarding buddy for AI-native engineering teams.

At OpenAI’s Codex team, the onboarding buddy is Codex. If you’re building AI-native engineering teams in your organization, it can be something else.

Here is an example that Thibault Sottiaux, Engineering Lead for OpenAI's Codex team, shared in our recent conversation:

"Codex walks new hires through onboarding, sets up their entire computer, and helps them understand the codebase, projects, and existing features. It basically acts like a highly skilled engineering mentor.

From initial setup to being productive, most of the onboarding process is handled by the AI tool. As a result, onboarding is much faster than it used to be."

At OpenAI, they also have a strong culture of shipping on day one, and that’s recommended for AI-native engineering teams as well. It’s really important for every engineer to provide value as soon as possible.

Normal traditional teams have the goal to ship code to production within the first week. In AI-native engineering teams, that should be on the first day.

Learn how to build AI-native engineering teams in this Engineering Leadership article: https://lnkd.in/d5ambTDW

How do the teams behind ChatGPT actually work?

I recently had the pleasure of speaking with Sulman Choudhry, Head of Engineering, ChatGPT.

The organization is much smaller and operates very differently from most traditional tech companies.

Teams are highly technical, roles often overlap, and many ideas start from the bottom up rather than from top-down.

At the same time, engineers frequently own problems end-to-end, research and product development happen side by side, and experimentation is a core part of their process.

Learn more about OpenAI's engineering culture in the latest Engineering Leadership article: https://lnkd.in/dJCFKQ2C

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