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

Software engineers invented AI for one sole purpose:

To do front end development.

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! Learn more here: https://lnkd.in/gfgvf9fQ

Most people don’t cook their own food.
Most people don’t cut their own hair.
Most people don’t fix their own car.

But everyone’s a software engineer now because AI exists.

undefined undefined

Author

Chief Executive Officer

Podcast Host

Founder, CEO, CTO

Remember when we spent all day on Stack Overflow trying to find solutions to weird bugs? 

Now we spend all day arguing with AI about those same weird bugs.

The only difference?

Nobody is roasting us along the way.

Instead, AI hallucinates half the time... and the other half it’s just a yes-man.

It is sad that Stack Overflow has lost 98% of its traffic and is nearly abandoned. 

The OG co-pilot is gone.

What we really need in 2026 is a “Stack Overflow mode” in AI.

It won’t just tell you how to fix your code.

It will roast you first.

At standup, an engineer is explaining a bug.

My immediate thought:
It would take less time to ask Claude to fix this than to have this conversation.

That’s becoming normal.

More and more, explaining the problem takes longer than fixing it.

In some ways, that’s always been true. Teams have spent years writing JIRA tickets, planning work, and aligning on tasks that take minutes to execute.

But here’s the part AI doesn’t fix.

When people don’t understand the product, the intent, or the outcome they own, everything slows down.
They ask for clarification.
They ask for confirmation.
They wait.

That’s the real bottleneck.

AI can help you write code faster.
It can’t give someone ownership they don’t have or context they were never given.

If your team needs a meeting to understand what “done” looks like, AI won’t save you.
It will just make the gap more obvious.

Everyone says they want engineers who think like owners… until they actually get one.

We talk about wanting “10x engineers” or “product-minded developers.”
The ones who don’t just code. They question requirements, push back on bad ideas, and care about what happens after the code ships.

Here’s what I’ve learned after 20+ years building teams:

Most companies aren’t actually ready for engineers who think.

Because engineers who think like owners don’t just follow the sprint plan.
They ask uncomfortable questions:

❓ “Why are we building this?”
❓ “What problem does this actually solve?”
❓ “Have we talked to any users about this?”

They challenge the roadmap.
They skip meetings that waste time.
They push for clarity when everyone else just nods along.

And that makes people nervous.

We say we want ownership, but we build systems that reward compliance.
We preach innovation, then enforce rigid process.
We hire for product thinking and hand them a backlog of predetermined solutions.

I wrote Product Driven because this disconnect is killing teams.

You can’t build great products with engineers who just take tickets.
But you also can’t expect product thinking in a culture that punishes it.

And now AI removes the last excuse.

AI won’t replace engineers.
But it will expose teams that never learned how to think.

When execution gets cheap, judgment becomes the bottleneck.

Real product engineers need:

☑️ Context, not just requirements
☑️ Problems to solve, not features to ship
☑️ Permission to say “this doesn’t make sense”
☑️ Leaders who value outcomes over output

If you want engineers who think like owners, you have to lead like you trust them.

Otherwise, you don’t want product thinkers.
You want expensive task-runners.

And AI will make that painfully obvious.

I wrote Product Driven for leaders who want engineers thinking in outcomes, not just shipping code.

If that’s the team you want, start here:

https://lnkd.in/gfgvf9fQ

If you can’t write code without AI, you shouldn’t be shipping code to production.

At least not for real-world business applications.

We still need a software engineer in the loop. Someone who understands tradeoffs, edge cases, failure modes, and the cost of being wrong when real users and real revenue are involved.

That hasn’t changed.

This is no different than DIY home remodeling versus professional work.

You can watch videos.
You can follow instructions.
You can even make it look right.

But the inspection still matters.

Because mistakes don’t show up immediately.
They show up later.
When you’re no longer looking.

And when the house catches fire, the problem isn’t how confident you felt doing the work.

It’s that you were the one who signed off on it.

Sometimes DIY is fine.
Sometimes you need rigorous engineering.

Here’s the distinction that matters.

Production isn’t just your house.
It’s a house someone else is paying for and depending on.

That’s the bar.

“Works on my machine” isn’t good enough for production, even if the code was written by AI.

Why are software engineers more worried about AI-generated code than copying snippets from StackOverflow?

We spent years relying on StackOverflow to solve problems we couldn’t solve ourselves.

And we didn’t feel embarrassed about it.
We felt smart.

Because the point was never who wrote the code.
The point was getting input from people who were better than us at that problem.

That’s what StackOverflow really was.
Borrowed judgment.
Collective experience.
Patterns refined by people who had already made the mistakes.

Now that same behavior has a new interface.
We call it AI.

And suddenly people act like this is a completely different risk.

Nothing changed.

We’ve always stood on the shoulders of people smarter than us.
We’ve always reused ideas we didn’t invent.
We’ve always depended on outside intelligence to move faster.

The rule then is the rule now.

You don’t ship code you don’t understand, test, or review.

StackOverflow didn’t absolve you of responsibility.
AI doesn’t either.

Culture matters even more when your business is the team.

This is our second year in a row being Great Place to Work Certified at Full Scale, with 95% of our team saying this is a great place to work.

That matters for one simple reason.
We do staff augmentation.

At Full Scale, our clients are not hiring us for a short-term engagement.
They are trusting us with their long-term teams.

That means hiring and retaining the best talent in the Philippines is not optional.
It is the foundation of everything we do.

We consistently retain 93%+ of our employees.
That stability shows up where it matters most. Our clients get reliable teams, deep product knowledge, and momentum that does not reset every six months.

You cannot fake that with perks or slogans.
It comes from the day-to-day reality:

✅ Clear expectations
✅ Real growth paths
✅ Respect for people’s time and careers
✅ Leaders who protect the team, not just the roadmap

Culture is not what leadership says.
It is what people choose to stay for.

Proud of this team.
And committed to earning that trust again next year.

Need help scaling your software engineering team? We have helped over 200 other companies and can help you grow with affordable talent.

Full Scale

Tailwind didn’t “lay off 75% of its team” because of AI.

They laid three people off because they’re trying to run three business models at once.

They behave like:
⚡️ an open source community project
⚡️ a sponsor-supported public good
⚡️ a commercial product company

All at the same time.

Their homepage feels like open source.
They ask for sponsors.
They quietly link to a paid product.

Each of those choices can make sense on its own.
Together, they create confusion.

Sponsorships say, “Support us if you want.”
Sales say, “This creates value worth paying for.”

Trying to do both, without clearly separating those stories, leaves customers unsure how to engage and teams unsure what they’re building toward.

That’s not an AI problem.
It’s a go-to-market identity problem.

Did you even know they had paid products?

"Vibe coding is creating a bunch of software held together by duct tape that isn't secure"


Who is going to tell them?

“I used an offshore team and had to rework the entire project.”

I hear this all the time.

And almost every time, the problem wasn’t offshore development.

It was how it was done.

When this fails, it’s usually one of two things.

#1: No technical leadership

This hits non-technical founders the hardest.

They don’t know how software should be built, so they fully trust the lowest-cost dev shop they can find.

That’s not outsourcing efficiency.
That’s abdication.

Every company needs technical leadership, even if it’s fractional.
A CTO, a senior architect, or a trusted technical advisor who actually owns the outcome.

Without that, you’re just hoping the other side gets it right.

#2: Treating offshore like a separate team

The right way to do offshore development is integration, not delegation.

You can’t throw work over the wall and expect quality back.

The best results I’ve seen came from a hybrid model:

⚡ Local developers as team leads
⚡ Offshore developers embedded into the same workflows
⚡ One team, one set of standards, one shared outcome

When something wasn’t right, we knew that week, not at the end of the project when rework was inevitable.

Most companies go offshore to save money.

That’s fine.

But the savings only show up when you do it right.

That means:

⚡ Technical leadership in-house
⚡ A staff augmentation model, not project outsourcing
⚡ Treating offshore developers like real teammates, not a vendor

If your team works well remotely, it will work well globally too.

Need help doing it the right way?

Full Scale can help. We have helped over 200+ companies scale their engineering teams with high quality global talent from the Philippines.

As humans, we often overvalue the things we don’t know how to do...

And undervalue the things we already know how to do because they seem easy.

That’s why so many of us aren’t happy.

We lose sight of the amazing things we can already do.

And if you have ADHD like me, you get bored, crave novelty, and chase the next challenge.

Sometimes that curiosity is a gift.
Sometimes it feels like a curse.

“Keeping our headcount low is key. One way to do that is heavily leveraging AI.”

That’s not a hot take.
It’s a sentence straight out of a startup CEO’s investor update.

At a startup, keeping costs down is survival, not strategy.

But this thinking didn’t stay in startups.
It’s now hitting the entire job market.

Some roles are going away because AI can do the work, or at least do it good enough.
Other roles are opening up because AI enables things we simply couldn’t do before.

That’s how every major technology shift works.

New capabilities emerge.
The job market adjusts.

From the cotton gin to computers to AI.
Change is constant.

At Full Scale, we’ve never been busier for our clients, and we’re hiring 100 software engineers this year just to keep up.

I’ll be honest... we don’t have it all figured out either.

Some of our engineers love working with AI.
Others are understandably cautious about how it’s changing their careers and the craft they care about.

If you’re wrestling with that tension right now, you’re not behind.

We’re all living through this transition together.

"This field is done for."

I keep seeing this every day.

There's a big difference between writing code and knowing why you're writing that code.

The people who don't know why they're writing the code, they might be done for. 

The people with this alarmist attitude are wearing me out though.

At Full Scale, we are hiring multiple software engineers this month. 

This field isn't done for. It's changing.

Being lazy is my superpower.

I hate it when things are hard.

I am on an endless pursuit to find an easier way.

The reality is most people do not think this way, or they have been conditioned not to.

They are used to working in big companies or cultures where things are done in weird ways, and they feel like they have no control to change them.

So they maintain the status quo and try not to rock the boat. Speaking up feels rude. Or like questioning authority.

This is also why innovation dies.

You hear it everywhere.
“I don’t know why we do it this way. It’s how we’ve always done it.”

My brain goes somewhere else.
“That way sucks. We should change it.”

So I speak up.

And that is why I am an entrepreneur.

And why I am unemployable.

Because I’m too lazy to do things the hard way when there’s a better way.

Linus Torvalds is showing why AI creates more software engineering, not less.

When something gets more affordable, we tend to use more of it, not less of it.

Why?

Because we can apply it to more things at a lower cost.

Here’s a great example from Linus:

Think about security patches for frameworks or operating systems.

Patching old versions is a nightmare.

The code has changed over years.
The people who understood it are gone.

You can take recent security changes and port them back to an older, stable codebase that still runs real production systems.

That’s an ideal job for AI.

More affordable software engineering won’t reduce the amount of work.

We will find more use cases and get things done we didn't have time to do before.

Most engineering leaders don’t fail with offshore software engineers because of talent.

They fail because they hire task executors, which just creates more work for them, not less.

What leaders really need is people they can trust to own projects and get things off their plate.

Here’s what I hear on almost every call with leaders trying offshore dev:

“They need approval for every decision.”
“I still had to do all the architectural thinking.”
“They delivered what we asked for… it just wasn’t great.”

That’s not a people problem.

It’s a role clarity and leadership problem.

If you don’t clearly define:

• What technical decisions someone owns
• What outcomes they’re accountable for
• How success is measured beyond “code shipped”

You won’t get leverage. You’ll get people who need to be micromanaged.

The leaders that struggle don’t lack access to good engineers. They lack a system that turns engineers into owners instead of dependency points.

If you want offshore engineers to reduce your workload instead of adding to it, DM me.

AI has fundamentally changed how websites get built.

What used to be fumbling around in website builders just to ship a basic marketing site is gone.

Tools like Lovable are honestly mind-boggling. And it’s clear this is just the beginning.

Website design has crossed a line it won’t come back from.

Some people will argue AI isn’t the "best" at design or graphics. I actually agree.

But I don’t need best. I need "great enough" and easy.

And that’s what most people actually need too.

For most of my career, I was the CTO people called when things weren’t working.

Sales made a promise they couldn’t keep. Production was down. A bug nobody could fix.

I was the SWAT team you called for help.

I fixed systems, kept teams moving, and lived in the gap between what the company promised and what the team could realistically deliver. For a long time, I thought that gap was just part of being a CTO.

It wasn’t.

What we really needed was scale. I couldn’t be involved in every decision anymore. I needed affordable talent I could trust, and the confidence to delegate work that actually mattered.

I’d heard all the horror stories about offshore teams, and I’d lived a few of them myself.

So when I tried to solve this for my own company, I didn’t assume offshore would magically work. I treated it like any other critical system: something that had to be designed, tested, and managed deliberately.

I needed to hire about ten software engineers and figured out how to do it in a way that actually worked, including building a team in the Philippines I could rely on.

That’s when people started noticing.

Friends around Kansas City asked how I was pulling it off, then their companies did too.

By the end of the first year, I had hired more than 100 engineers, mostly for other founders dealing with the same constraints.

Full Scale became a company almost by accident, built out of necessity rather than ambition.

The advantage of building it after years as a CTO is that I don’t guess what technical leaders need. I’ve lived the tradeoffs, made the offshore mistakes firsthand, and learned what it takes to make it work at scale.

Sometimes the best companies don’t come from chasing an idea.

They come from solving your own problem well enough that others ask you to solve it for them.

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

In today’s AI era, being a great engineer is no longer defined by your individual contribution alone.

Being a great engineer is defined by the ability to make others around you better, to deliver projects successfully, and to create impact that moves the whole organization forward.

There are many ways to multiply your efforts:
- making your whole team better,
- to work well across different teams,
- be a multiplier through systems,
- multiply and scale through people.

These are all archetypes of multipliers, where each provides immense impact in its own specific way.

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

CTO | Founder of Engineering Leadership newsletter (180k+ subscribers) - Helping you become a great engineering leader!

This is what happens when you do 10k lines of code per day with AI :)

Happy Friday and have a good weekend!

The speed of writing code has never been an issue.

You can write 10,000 or 20,000 lines of code, but if you are building the wrong thing for the user, it means nothing.

Once you know you’re building the right things, then you can bring in AI tools to speed up the delivery.

This is far more important than just typing faster or producing more code.

While most teams are bragging about how fast they ship code, your focus should be somewhere else:

Focus on building the right things, first.

Then, use technology to accelerate the rate at which you produce new value.

That's how you win in tech in 2026.

What makes a great engineer in the AI-era?

As tools for development got better, the desired skills for engineers have moved from just pure knowing programming languages and frameworks (expected) to problem-solving abilities and people skills.

It's less about the tasks, but how big an impact you can create.

And engineers who can create the biggest impact have the multiplier mindset.

It's the mindset where you focus less on just your pure individual tasks, but you focus on how you can make things better for your whole team, organization, and, of course, the customers (users).

Learn more in this Engineering Leadership article: https://lnkd.in/ezWydu8T

As an engineer, I didn't properly use 1:1 meetings with my manager.

I didn't use my 1:1 meetings for my career growth, instead, I used them to give a status update.

I was missing out on so many different learning opportunities, which I could use the 1:1 meeting for:
- I could talk about career goals,
- work through plans for the promotion or
- ask for specific coaching and feedback situations.

Lesson learned: 
It’s a lot better to use 1:1 meetings to help you be successful in your role and with your overall career growth.

P.S. To learn more -> join engineers and leads from Meta, Google, IBM, Revolut in my cohort-based course here: https://lnkd.in/exsYfM54 (24 hours left to sign up!).

Meetings don’t create alignment. Decisions do.

If no one leaves the room knowing:
- what was decided
- who owns it
- what happens next

It wasn’t a meeting, it was a calendar placeholder.

Make sure to respect people’s time.

End meetings with decisions, or don’t have them at all.

Learn how to create meetings that engineers will love in this Engineering Leadership article: https://lnkd.in/ekZcdgGu

“My work speaks for itself”

Assuming that everyone knows that you are doing good work is in most cases, going to be a wrong assumption.

There are 2 things I like to say.

1. You can do the best work in the world, but if the expectations are not managed correctly, it will not be perceived that way.

2. Doing great work is only half of the work, making sure that everyone knows about it is the other half. You need to do both.

Learn how to build a successful engineer <> manager relationship in the latest Engineering Leadership article that we did together with Shehab Abdel-Salam here: https://lnkd.in/dhJ4qAyD

Coinbase scaled their hiring to 150 engineers per month over the pandemic.

How did they do it?

1. They standardized the searches

Previously, for every new hire, it would be required to draft a “job spec”. With standardization, you could only hire a frontend engineer, a backend engineer, a crypto engineer, an infra engineer, or an ML engineer (and a few others).

2. They moved from hiring in SF only to nationwide

And later to 10+ countries. This resulted in a massive increase in talent pool size.

3. Interviewing became centralized

Candidates weren't screened by their future hiring manager or peers anymore, but rather by any Coinbase engineer. This massively opened up the interviewer capacity.

4. From hiring manager round-ups to centralized hiring panels

Each day with a rotation, one Coinbase leader would host a hiring panel (with 5-6 senior managers), where all the interview packets feedbacks were reviewed and decided which ones would move to offer and which ones would be rejected.

This gave them the ability to process 40-50 candidates at the pre-offer stage a day.

5. The offers became standardized and non-negotiable

No back and forth with the candidate. Both base salary and equity were directly related to the level you would be hired in, no exceptions allowed.

To learn more, make sure to not miss tomorrow's Engineering Leadership newsletter.

Luca Bonmassar, Former VP of Engineering at Coinbase, is sharing a lot of interesting insights on how they scaled hiring.

To not miss tomorrow's article, make sure to join 180k+ engineering leaders here: https://lnkd.in/eNSKBMUf

Structure stories with CARL instead of STAR for a behavioral interview.

This is what Austen McDonald recommends, and here is his reasoning.

CARL stands for:
Context: The background of what was happening and why it mattered
Actions: The specific steps you took (plural, not singular)
Results: What changed and the impact you made
Learnings: What you learned or would do differently

The key difference is that CARL combines Situation and Task into Context, eliminating the mental energy wasted trying to separate them.

And it adds Learnings, which gives you a platform to demonstrate the depth of wisdom that separates senior engineers from less experienced ones.

Learn all about how to nail behavioral interviews in the latest Engineering Leadership article: https://lnkd.in/eN5qJy7g

taplio

The Battle

Matt Watson

Gregor Ojstersek

Followers

Posts

Engagements

Likes

Performance ScoreMatt Watson's LinkedIn Performance

Performance ScoreGregor Ojstersek's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Matt Watson

Gregor Ojstersek

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Matt Watson's LinkedIn Performance

Performance ScoreGregor Ojstersek's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreMatt Watson's LinkedIn Performance