A decade ago, Slash began as an ambitious Agile consulting company with a venture studio dream and a genuine belief that culture and craft could coexist. Today, as a global AI-first digital studio, the people who built it are still here, and still learning.
In this session, our Co-Founder and COO, Alex, shares 10 hard-won lessons on trust-first remote leadership, navigating the AI transformation from the inside, and what it really means to build great engineering teams over ten years.
The Flashback
“When you first joined Slash, what did you honestly think this place was going to be, and how far off was your initial guess?”
When I first joined Slash, I expected three things: a company with a genuine culture that cared about people, passionate teams who loved what they were building, and ambitious leaders who wanted to create something bigger than themselves. I also joined because I was excited by the combination of a venture studio and a high-quality Agile consulting company.
Looking back, I wasn’t actually that far off.
The culture, the passion, and the ambition were there every day. You could feel it in our projects, our discussions, and even in the difficult strategic decisions about where Slash should go next.
One expectation that evolved differently was the venture studio. I imagined that after ten years we would have built a much larger portfolio of startups. Instead, Slash chose a different path. We focused more on building meaningful partnerships and sustainable businesses rather than simply growing the number of ventures. Looking back, I think that reflected our values well.
What surprised me the most wasn’t that Slash changed, it was how continuously it kept transforming. Our Agile practice kept evolving, and today we’re once again reinventing how we deliver software because of AI. That ability to keep adapting has always been one of Slash’s biggest strengths.
The Global & Remote Reality
“After years of working remotely across different cultures, languages, and time zones, what’s the biggest lesson you’ve learned about making global teamwork actually work?”
After years of working remotely, I’ve become convinced that almost everything starts with just two things: trust and ownership.
Ownership matters because remote work simply doesn’t work if people need to be constantly monitored. People need to feel responsible for the outcomes they own, not just the hours they spend working.
Trust matters just as much. When people trust each other, conversations change. Instead of asking, “Are you working enough?”, the discussion becomes, “What’s blocking you, and how can we help?” At Slash, we never tried to track whether people sat at their computer for eight hours a day. We focused on the work, the collaboration, and the outcomes.
Of course, those two principles aren’t enough on their own. You also need leaders who truly embrace remote work, a culture that supports it, good communication habits, solid work practices, and an environment where different cultures are respected.
But I genuinely believe trust and ownership are the foundation that everything else is built upon.
Real Talk on AI
“Now on AI. Where do you land on AI right now? How much has it actually involved into your daily routine and workflow?”
AI is now fully integrated into my daily habits, both at work and in my personal life.
The biggest change isn’t simply that AI helps me work faster. It helps me extend what I’m able to do. There are projects I probably wouldn’t have started before, not because I wasn’t capable of doing them, but simply because I didn’t have enough time to prioritize them. AI lets me work on things I otherwise wouldn’t have had time to pursue, allowing me to explore more ideas, work across more domains, and extend myself into areas I wouldn’t have reached before.
I also use AI as a learning partner, not just an execution tool. I regularly challenge its answers, ask different AI models to critique and challenge each other’s responses, and then use my own judgment to decide what makes sense. When I brainstorm with AI, it isn’t about accepting the first answer as truth, it’s about creating better thinking through discussion.
Over time, I’ve also automated more and more of my own workflows. I build systems, document important projects so they can easily be picked up by different AI models, and increasingly take advantage of each model’s unique strengths instead of relying on only one.
Compared to last year, I now use Agentic AI much more heavily. Having AI agents working on tasks while I’m focused on something else, or even while I’m sleeping, has fundamentally changed how I approach productivity.
There are downsides too. AI evolves so quickly that it’s impossible to keep up with everything. I’ve accepted that I can’t learn every new tool or experiment with every new model. Instead, I focus on becoming really good at the AI capabilities that immediately benefit me while staying aware of what’s coming next.
AI also never really runs out of work, the next project or task is always just one prompt away. Learning when to stop, and to deliberately de-prioritize work that AI could help me do, has become just as important as learning how to use AI effectively.
Product Evolution
“What’s the single biggest insight you’ve gained about building products over the years, and how does building something today in 2026 compare to back in 2016?”
If I had to pick one lesson after ten years, it would be this: products don’t build themselves, great teams do.
Technology changes incredibly fast. Frameworks come and go, AI changes how we write software, and products constantly evolve. What stays is the people and the engineering culture behind them.
That’s why I’ve always invested heavily in building strong teams and strong engineering practices. Teams solve today’s problems. Engineering practices make sure products remain sustainable as people join, leave, and the business evolves.
Compared to 2016, engineering has moved much closer to the business. Software teams are no longer simply implementing requirements, they’re increasingly helping shape products, influence business decisions, and create value alongside the rest of the organization.
More recently, AI has accelerated another evolution. We’re seeing the rise of builder roles, including concepts like Forward Deployed Engineers, where engineers combine deep technical skills with product thinking and direct customer engagement. Increasingly, engineers aren’t just building software, they’re identifying problems, validating ideas, rapidly prototyping solutions with AI, and helping shape the business itself. I believe we’ll continue seeing more of these hybrid roles over the coming years.
Managing High-Powered Devs
“Across your time as Head of Delivery, CTO, and COO, you led some of the sharpest technical minds at Slash. What’s your playbook for aligning smart, opinionated developers without stifling their creativity?”
I don’t think there’s one leadership style that works for everyone.
People are at different stages of their careers and are motivated by different things. Some want to become leaders, some want to grow their business knowledge, some want to become deep technical experts, and others are still discovering where they want to go. My role was always to adapt my leadership to help each person grow.
A combination of teaching, mentoring, coaching, empowerment, and helping people build a long-term growth vision has worked well for me.
It also comes back to trust and care. I genuinely believe most people want to do good work and want to improve. Mistakes shouldn’t immediately be seen as failures but as opportunities to learn and become better.
As for creativity, I think it naturally grows when people have trust, ownership, and the freedom to experiment.
Finally, one lesson I’ve learned is to invest early in future leaders. When you help great people become leaders themselves, they naturally spread the same mindset across the organization, and that’s how healthy engineering cultures continue to grow.
Driving the AI Transformation
“You were instrumental in pushing Slash to embrace AI and directing us toward becoming an AI-driven company. How did you get the team to move past the initial hype and actually integrate AI into their core workflows?”
To be honest, getting people excited about AI and staying excited about it wasn’t the difficult part.
Slash has always attracted people who enjoy exploring new technologies, and many team members were already experimenting with AI long before we formally launched our transformation initiatives.
The real challenge was turning lots of individual experiments into something sustainable across the whole company.
For our larger Delivery organization, that meant building AI into our engineering processes and delivery lifecycle so teams could work consistently instead of everyone inventing their own way of working.
For smaller teams, it was more about giving people the right tools, guidance, and time to explore how AI could genuinely improve their work.
Not everything we tried was worth keeping, and that’s completely normal. Even the practices we adopted a year ago are already evolving.
I don’t think the transformation driven by AI is going to slow down anytime soon. We’ll probably keep evolving for many years to come.
Future of AI
“Open-source models like DeepSeek, Qwen, Kimi, and Llama are rapidly closing the gap with proprietary frontier models from companies like OpenAI, Anthropic, and Google. How do you see the AI ecosystem evolving, and where should developers place their bets over the next few years?”
Open-source AI is one of the strengths of today’s AI ecosystem. It lowers the barrier to innovation and gives startups, researchers, and companies much more freedom to experiment, build, and compete. I think that healthy competition will continue pushing the whole industry forward.
Looking ahead, I think AI is evolving in at least three major directions.
The first is intelligence itself. A few years ago, AGI, and even more so ASI, felt like moonshots that belonged more in science fiction than engineering discussions. Today, progress is accelerating so quickly that they feel increasingly achievable. Whether that happens this year or over the next several years or decades is impossible to know, but it’s exciting to see ideas that once felt purely theoretical becoming realistic engineering goals.
The second is accessibility. Smaller language models are improving incredibly quickly. We’re reaching a point where capable AI won’t only run inside massive cloud infrastructure, but also directly on laptops, phones, robots, and many other edge devices. As these models become more efficient and autonomous, they’ll power an entirely new generation of AI-enabled products and experiences.
The third is AI’s impact on other industries. As AI capabilities continue improving, I think we’ll see major breakthroughs across robotics, biotech, healthcare, manufacturing, education, and even space. AI won’t just improve existing workflows, it will unlock entirely new ways of solving problems and accelerate innovation across many industries.
For developers, my advice is simple: keep learning. We’re still in the middle of one of the biggest disruptions our industry has ever experienced. We went from AI-assisted coding, to code generation, to agentic development, and now we’re seeing engineers becoming builders who combine engineering, product thinking, and AI into one role. The people who stay curious, continue learning, and adapt alongside the technology will create the most value over the coming years.
The Hard Lessons
“Is there a specific moment where something went badly wrong, and what did you learn from picking up the pieces?”
There isn’t one single moment that stands out, but one lesson that really stayed with me was learning the reality of growing a business.
We invested heavily in building a great culture, strong engineering practices, and high-quality delivery. Those things are incredibly important, but they’re not enough on their own. You still need a healthy sales pipeline, new clients, differentiation, and the ability to make difficult business decisions.
At one point, we also tried to push too many new initiatives at the same time. Some of them had real potential, but we simply spread ourselves too thin and couldn’t give each one the attention it deserved.
One of my biggest lessons was learning to focus. We adopted a stronger OKR approach, became more disciplined about priorities, and learned to stop initiatives that weren’t working instead of holding onto them for too long.
The Slash Secret
“What do you think genuinely sets Slash apart from every other tech company out there?”
For me, it’s the people.
Even after all these years, Slash has remained a company where people genuinely know and care about each other. Even though we’re fully remote, it has never felt like everyone was working alone.
The willingness to help each other, share knowledge, and support each other’s growth has allowed us to grow as individuals, deliver better work for our clients, and continuously evolve as a company.
The One-Liner
“Ten years in, summarized in a single sentence: What is Slash to you?”
A place where people genuinely care, continuously learn, embrace innovation, and challenge each other to become a little better every day.
